PS D:\Downloads\ABDM\Compressed\llama-b8533-bin-win-cuda-12.4-x64>
PS D:\Downloads\ABDM\Compressed\llama-b8533-bin-win-cuda-12.4-x64> & "D:\Downloads\ABDM\Compressed\llama-b8533-bin-win-cuda-12.4-x64\llama-cli.exe" `
>>   -m "D:\Documents\Github\ai-backend-studio\models\Qwen3.5-4B-Abliterated-Claude-4.6-Opus-Reasoning-Distilled.i1-Q6_K.gguf" `
>>   -p "Olá! Me diga quem você é em uma frase." `
>>   -n 80 `
>>   --n-gpu-layers 999 `
>>   --flash-attn on `
>>   --ctx-size 4096 `
>>   --no-display-prompt `
>>   -v
load_backend: loaded RPC backend from D:\Downloads\ABDM\Compressed\llama-b8533-bin-win-cuda-12.4-x64\ggml-rpc.dll
load_backend: loaded CPU backend from D:\Downloads\ABDM\Compressed\llama-b8533-bin-win-cuda-12.4-x64\ggml-cpu-haswell.dll
build: 8533 (0fac87b15) with Clang 19.1.5 for Windows x86_64

Loading model... |srv    load_model: loading model 'D:\Documents\Github\ai-backend-studio\models\Qwen3.5-4B-Abliterated-Claude-4.6-Opus-Reasoning-Distilled.i1-Q6_K.gguf'
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
llama_params_fit_impl: getting device memory data for initial parameters:
llama_model_loader: loaded meta data with 53 key-value pairs and 426 tensors from D:\Documents\Github\ai-backend-studio\models\Qwen3.5-4B-Abliterated-Claude-4.6-Opus-Reasoning-Distilled.i1-Q6_K.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = qwen35
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Qwen3.5-4B-Abliterated-Claude-4.6-Opu...
llama_model_loader: - kv   3:                            general.version str              = 4.6
llama_model_loader: - kv   4:                           general.finetune str              = Abliterated-Claude-Opus-Reasoning-Dis...
llama_model_loader: - kv   5:                           general.basename str              = Qwen3.5
llama_model_loader: - kv   6:                         general.size_label str              = 4B
llama_model_loader: - kv   7:                            general.license str              = other
llama_model_loader: - kv   8:                   general.base_model.count u32              = 1
llama_model_loader: - kv   9:                  general.base_model.0.name str              = Qwen3.5 4B Claude 4.6 Opus Reasoning ...
llama_model_loader: - kv  10:               general.base_model.0.version str              = 4.6
llama_model_loader: - kv  11:          general.base_model.0.organization str              = Jackrong
llama_model_loader: - kv  12:              general.base_model.0.repo_url str              = https://huggingface.co/Jackrong/Qwen3...
llama_model_loader: - kv  13:                               general.tags arr[str,5]       = ["abliterated", "de-censored", "reaso...
llama_model_loader: - kv  14:                         qwen35.block_count u32              = 32
llama_model_loader: - kv  15:                      qwen35.context_length u32              = 262144
llama_model_loader: - kv  16:                    qwen35.embedding_length u32              = 2560
llama_model_loader: - kv  17:                 qwen35.feed_forward_length u32              = 9216
llama_model_loader: - kv  18:                qwen35.attention.head_count u32              = 16
llama_model_loader: - kv  19:             qwen35.attention.head_count_kv u32              = 4
llama_model_loader: - kv  20:             qwen35.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
llama_model_loader: - kv  21:                      qwen35.rope.freq_base f32              = 10000000.000000
llama_model_loader: - kv  22:    qwen35.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  23:                qwen35.attention.key_length u32              = 256
llama_model_loader: - kv  24:              qwen35.attention.value_length u32              = 256
llama_model_loader: - kv  25:                     qwen35.ssm.conv_kernel u32              = 4
llama_model_loader: - kv  26:                      qwen35.ssm.state_size u32              = 128
llama_model_loader: - kv  27:                     qwen35.ssm.group_count u32              = 16
llama_model_loader: - kv  28:                  qwen35.ssm.time_step_rank u32              = 32
llama_model_loader: - kv  29:                      qwen35.ssm.inner_size u32              = 4096
llama_model_loader: - kv  30:             qwen35.full_attention_interval u32              = 4
llama_model_loader: - kv  31:                qwen35.rope.dimension_count u32              = 64
llama_model_loader: - kv  32:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  33:                         tokenizer.ggml.pre str              = qwen35
llama_model_loader: - kv  34:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  35:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...                                                                                                       -llama_model_loader: - kv  36:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  37:                tokenizer.ggml.eos_token_id u32              = 248046
llama_model_loader: - kv  38:            tokenizer.ggml.padding_token_id u32              = 248055
llama_model_loader: - kv  39:                    tokenizer.chat_template str              = {%- if tools %}\n    {{- '<|im_start|>...
llama_model_loader: - kv  40:               general.quantization_version u32              = 2
llama_model_loader: - kv  41:                          general.file_type u32              = 18
llama_model_loader: - kv  42:                                general.url str              = https://huggingface.co/mradermacher/Q...
llama_model_loader: - kv  43:              mradermacher.quantize_version str              = 2
llama_model_loader: - kv  44:                  mradermacher.quantized_by str              = mradermacher
llama_model_loader: - kv  45:                  mradermacher.quantized_at str              = 2026-03-10T09:59:38+01:00
llama_model_loader: - kv  46:                  mradermacher.quantized_on str              = nico1
llama_model_loader: - kv  47:                         general.source.url str              = https://huggingface.co/Abhiray/Qwen3....
llama_model_loader: - kv  48:                  mradermacher.convert_type str              = hf
llama_model_loader: - kv  49:                      quantize.imatrix.file str              = Qwen3.5-4B-Abliterated-Claude-4.6-Opu...
llama_model_loader: - kv  50:                   quantize.imatrix.dataset str              = imatrix-training-full-3
llama_model_loader: - kv  51:             quantize.imatrix.entries_count u32              = 248
llama_model_loader: - kv  52:              quantize.imatrix.chunks_count u32              = 319
llama_model_loader: - type  f32:  177 tensors
llama_model_loader: - type q6_K:  249 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q6_K
print_info: file size   = 3.22 GiB (6.57 BPW)                                                                                                                                                                                              \init_tokenizer: initializing tokenizer for type 2
load: 0 unused tokens
load: control token: 248062 '<|fim_suffix|>' is not marked as EOG
load: control token: 248072 '<tts_pad>' is not marked as EOG
load: control token: 248050 '<|box_end|>' is not marked as EOG
load: control token: 248048 '<|object_ref_end|>' is not marked as EOG
load: control token: 248055 '<|vision_pad|>' is not marked as EOG
load: control token: 248060 '<|fim_prefix|>' is not marked as EOG
load: control token: 248049 '<|box_start|>' is not marked as EOG
load: control token: 248073 '<tts_text_bos>' is not marked as EOG
load: control token: 248052 '<|quad_end|>' is not marked as EOG
load: control token: 248054 '<|vision_end|>' is not marked as EOG
load: control token: 248075 '<tts_text_bos_single>' is not marked as EOG
load: control token: 248056 '<|image_pad|>' is not marked as EOG
load: control token: 248071 '<|audio_end|>' is not marked as EOG
load: control token: 248045 '<|im_start|>' is not marked as EOG
load: control token: 248047 '<|object_ref_start|>' is not marked as EOG
load: control token: 248051 '<|quad_start|>' is not marked as EOG
load: control token: 248053 '<|vision_start|>' is not marked as EOG
load: control token: 248057 '<|video_pad|>' is not marked as EOG
load: control token: 248061 '<|fim_middle|>' is not marked as EOG
load: control token: 248070 '<|audio_start|>' is not marked as EOG
load: control token: 248074 '<tts_text_eod>' is not marked as EOG
load: control token: 248076 '<|audio_pad|>' is not marked as EOG                                                                                                                                                                           |load: printing all EOG tokens:
load:   - 248044 ('<|endoftext|>')
load:   - 248046 ('<|im_end|>')
load:   - 248063 ('<|fim_pad|>')
load:   - 248064 ('<|repo_name|>')
load:   - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch                  = qwen35
print_info: vocab_only            = 0
print_info: no_alloc              = 1
print_info: n_ctx_train           = 262144
print_info: n_embd                = 2560
print_info: n_embd_inp            = 2560
print_info: n_layer               = 32
print_info: n_head                = 16
print_info: n_head_kv             = 4
print_info: n_rot                 = 64
print_info: n_swa                 = 0
print_info: is_swa_any            = 0
print_info: n_embd_head_k         = 256
print_info: n_embd_head_v         = 256
print_info: n_gqa                 = 4
print_info: n_embd_k_gqa          = 1024
print_info: n_embd_v_gqa          = 1024
print_info: f_norm_eps            = 0.0e+00
print_info: f_norm_rms_eps        = 1.0e-06
print_info: f_clamp_kqv           = 0.0e+00
print_info: f_max_alibi_bias      = 0.0e+00
print_info: f_logit_scale         = 0.0e+00
print_info: f_attn_scale          = 0.0e+00
print_info: n_ff                  = 9216
print_info: n_expert              = 0
print_info: n_expert_used         = 0
print_info: n_expert_groups       = 0
print_info: n_group_used          = 0
print_info: causal attn           = 1
print_info: pooling type          = -1
print_info: rope type             = 40
print_info: rope scaling          = linear
print_info: freq_base_train       = 10000000.0
print_info: freq_scale_train      = 1
print_info: n_ctx_orig_yarn       = 262144
print_info: rope_yarn_log_mul     = 0.0000
print_info: rope_finetuned        = unknown
print_info: mrope sections        = [11, 11, 10, 0]
print_info: ssm_d_conv            = 4
print_info: ssm_d_inner           = 4096
print_info: ssm_d_state           = 128
print_info: ssm_dt_rank           = 32
print_info: ssm_n_group           = 16
print_info: ssm_dt_b_c_rms        = 0
print_info: model type            = 4B
print_info: model params          = 4.21 B
print_info: general.name          = Qwen3.5-4B-Abliterated-Claude-4.6-Opus-Reasoning-Distilled
print_info: vocab type            = BPE
print_info: n_vocab               = 248320
print_info: n_merges              = 247587
print_info: BOS token             = 11 ','
print_info: EOS token             = 248046 '<|im_end|>'
print_info: EOT token             = 248046 '<|im_end|>'
print_info: PAD token             = 248055 '<|vision_pad|>'
print_info: LF token              = 198 'Ċ'
print_info: FIM PRE token         = 248060 '<|fim_prefix|>'
print_info: FIM SUF token         = 248062 '<|fim_suffix|>'
print_info: FIM MID token         = 248061 '<|fim_middle|>'
print_info: FIM PAD token         = 248063 '<|fim_pad|>'
print_info: FIM REP token         = 248064 '<|repo_name|>'
print_info: FIM SEP token         = 248065 '<|file_sep|>'
print_info: EOG token             = 248044 '<|endoftext|>'
print_info: EOG token             = 248046 '<|im_end|>'
print_info: EOG token             = 248063 '<|fim_pad|>'
print_info: EOG token             = 248064 '<|repo_name|>'
print_info: EOG token             = 248065 '<|file_sep|>'
print_info: max token length      = 256
load_tensors: loading model tensors, this can take a while... (mmap = false, direct_io = false)
load_tensors: layer   0 assigned to device CPU, is_swa = 0
load_tensors: layer   1 assigned to device CPU, is_swa = 0
load_tensors: layer   2 assigned to device CPU, is_swa = 0
load_tensors: layer   3 assigned to device CPU, is_swa = 0
load_tensors: layer   4 assigned to device CPU, is_swa = 0
load_tensors: layer   5 assigned to device CPU, is_swa = 0
load_tensors: layer   6 assigned to device CPU, is_swa = 0
load_tensors: layer   7 assigned to device CPU, is_swa = 0
load_tensors: layer   8 assigned to device CPU, is_swa = 0
load_tensors: layer   9 assigned to device CPU, is_swa = 0
load_tensors: layer  10 assigned to device CPU, is_swa = 0
load_tensors: layer  11 assigned to device CPU, is_swa = 0
load_tensors: layer  12 assigned to device CPU, is_swa = 0
load_tensors: layer  13 assigned to device CPU, is_swa = 0
load_tensors: layer  14 assigned to device CPU, is_swa = 0
load_tensors: layer  15 assigned to device CPU, is_swa = 0
load_tensors: layer  16 assigned to device CPU, is_swa = 0
load_tensors: layer  17 assigned to device CPU, is_swa = 0
load_tensors: layer  18 assigned to device CPU, is_swa = 0
load_tensors: layer  19 assigned to device CPU, is_swa = 0
load_tensors: layer  20 assigned to device CPU, is_swa = 0
load_tensors: layer  21 assigned to device CPU, is_swa = 0
load_tensors: layer  22 assigned to device CPU, is_swa = 0
load_tensors: layer  23 assigned to device CPU, is_swa = 0
load_tensors: layer  24 assigned to device CPU, is_swa = 0
load_tensors: layer  25 assigned to device CPU, is_swa = 0
load_tensors: layer  26 assigned to device CPU, is_swa = 0
load_tensors: layer  27 assigned to device CPU, is_swa = 0
load_tensors: layer  28 assigned to device CPU, is_swa = 0
load_tensors: layer  29 assigned to device CPU, is_swa = 0
load_tensors: layer  30 assigned to device CPU, is_swa = 0
load_tensors: layer  31 assigned to device CPU, is_swa = 0
load_tensors: layer  32 assigned to device CPU, is_swa = 0
create_tensor: loading tensor token_embd.weight
create_tensor: loading tensor output_norm.weight
create_tensor: loading tensor blk.0.attn_norm.weight
create_tensor: loading tensor blk.0.post_attention_norm.weight
create_tensor: loading tensor blk.0.attn_qkv.weight
create_tensor: loading tensor blk.0.attn_gate.weight
create_tensor: loading tensor blk.0.ssm_conv1d.weight
create_tensor: loading tensor blk.0.ssm_dt.bias
create_tensor: loading tensor blk.0.ssm_a
create_tensor: loading tensor blk.0.ssm_beta.weight
create_tensor: loading tensor blk.0.ssm_alpha.weight
create_tensor: loading tensor blk.0.ssm_norm.weight
create_tensor: loading tensor blk.0.ssm_out.weight
create_tensor: loading tensor blk.0.ffn_gate.weight
create_tensor: loading tensor blk.0.ffn_down.weight
create_tensor: loading tensor blk.0.ffn_up.weight
create_tensor: loading tensor blk.1.attn_norm.weight
create_tensor: loading tensor blk.1.post_attention_norm.weight
create_tensor: loading tensor blk.1.attn_qkv.weight
create_tensor: loading tensor blk.1.attn_gate.weight
create_tensor: loading tensor blk.1.ssm_conv1d.weight
create_tensor: loading tensor blk.1.ssm_dt.bias
create_tensor: loading tensor blk.1.ssm_a
create_tensor: loading tensor blk.1.ssm_beta.weight
create_tensor: loading tensor blk.1.ssm_alpha.weight
create_tensor: loading tensor blk.1.ssm_norm.weight
create_tensor: loading tensor blk.1.ssm_out.weight
create_tensor: loading tensor blk.1.ffn_gate.weight
create_tensor: loading tensor blk.1.ffn_down.weight
create_tensor: loading tensor blk.1.ffn_up.weight
create_tensor: loading tensor blk.2.attn_norm.weight
create_tensor: loading tensor blk.2.post_attention_norm.weight
create_tensor: loading tensor blk.2.attn_qkv.weight
create_tensor: loading tensor blk.2.attn_gate.weight
create_tensor: loading tensor blk.2.ssm_conv1d.weight
create_tensor: loading tensor blk.2.ssm_dt.bias
create_tensor: loading tensor blk.2.ssm_a
create_tensor: loading tensor blk.2.ssm_beta.weight
create_tensor: loading tensor blk.2.ssm_alpha.weight
create_tensor: loading tensor blk.2.ssm_norm.weight
create_tensor: loading tensor blk.2.ssm_out.weight
create_tensor: loading tensor blk.2.ffn_gate.weight
create_tensor: loading tensor blk.2.ffn_down.weight
create_tensor: loading tensor blk.2.ffn_up.weight
create_tensor: loading tensor blk.3.attn_norm.weight
create_tensor: loading tensor blk.3.post_attention_norm.weight
create_tensor: loading tensor blk.3.attn_q.weight
create_tensor: loading tensor blk.3.attn_k.weight
create_tensor: loading tensor blk.3.attn_v.weight
create_tensor: loading tensor blk.3.attn_output.weight
create_tensor: loading tensor blk.3.attn_q_norm.weight
create_tensor: loading tensor blk.3.attn_k_norm.weight
create_tensor: loading tensor blk.3.ffn_gate.weight
create_tensor: loading tensor blk.3.ffn_down.weight
create_tensor: loading tensor blk.3.ffn_up.weight
create_tensor: loading tensor blk.4.attn_norm.weight
create_tensor: loading tensor blk.4.post_attention_norm.weight
create_tensor: loading tensor blk.4.attn_qkv.weight
create_tensor: loading tensor blk.4.attn_gate.weight
create_tensor: loading tensor blk.4.ssm_conv1d.weight
create_tensor: loading tensor blk.4.ssm_dt.bias
create_tensor: loading tensor blk.4.ssm_a
create_tensor: loading tensor blk.4.ssm_beta.weight
create_tensor: loading tensor blk.4.ssm_alpha.weight
create_tensor: loading tensor blk.4.ssm_norm.weight
create_tensor: loading tensor blk.4.ssm_out.weight
create_tensor: loading tensor blk.4.ffn_gate.weight
create_tensor: loading tensor blk.4.ffn_down.weight
create_tensor: loading tensor blk.4.ffn_up.weight
create_tensor: loading tensor blk.5.attn_norm.weight
create_tensor: loading tensor blk.5.post_attention_norm.weight
create_tensor: loading tensor blk.5.attn_qkv.weight
create_tensor: loading tensor blk.5.attn_gate.weight
create_tensor: loading tensor blk.5.ssm_conv1d.weight
create_tensor: loading tensor blk.5.ssm_dt.bias
create_tensor: loading tensor blk.5.ssm_a
create_tensor: loading tensor blk.5.ssm_beta.weight
create_tensor: loading tensor blk.5.ssm_alpha.weight
create_tensor: loading tensor blk.5.ssm_norm.weight
create_tensor: loading tensor blk.5.ssm_out.weight
create_tensor: loading tensor blk.5.ffn_gate.weight
create_tensor: loading tensor blk.5.ffn_down.weight
create_tensor: loading tensor blk.5.ffn_up.weight
create_tensor: loading tensor blk.6.attn_norm.weight
create_tensor: loading tensor blk.6.post_attention_norm.weight
create_tensor: loading tensor blk.6.attn_qkv.weight
create_tensor: loading tensor blk.6.attn_gate.weight
create_tensor: loading tensor blk.6.ssm_conv1d.weight
create_tensor: loading tensor blk.6.ssm_dt.bias
create_tensor: loading tensor blk.6.ssm_a
create_tensor: loading tensor blk.6.ssm_beta.weight
create_tensor: loading tensor blk.6.ssm_alpha.weight
create_tensor: loading tensor blk.6.ssm_norm.weight
create_tensor: loading tensor blk.6.ssm_out.weight
create_tensor: loading tensor blk.6.ffn_gate.weight
create_tensor: loading tensor blk.6.ffn_down.weight
create_tensor: loading tensor blk.6.ffn_up.weight
create_tensor: loading tensor blk.7.attn_norm.weight
create_tensor: loading tensor blk.7.post_attention_norm.weight
create_tensor: loading tensor blk.7.attn_q.weight
create_tensor: loading tensor blk.7.attn_k.weight
create_tensor: loading tensor blk.7.attn_v.weight
create_tensor: loading tensor blk.7.attn_output.weight
create_tensor: loading tensor blk.7.attn_q_norm.weight
create_tensor: loading tensor blk.7.attn_k_norm.weight
create_tensor: loading tensor blk.7.ffn_gate.weight
create_tensor: loading tensor blk.7.ffn_down.weight
create_tensor: loading tensor blk.7.ffn_up.weight
create_tensor: loading tensor blk.8.attn_norm.weight
create_tensor: loading tensor blk.8.post_attention_norm.weight
create_tensor: loading tensor blk.8.attn_qkv.weight
create_tensor: loading tensor blk.8.attn_gate.weight
create_tensor: loading tensor blk.8.ssm_conv1d.weight
create_tensor: loading tensor blk.8.ssm_dt.bias
create_tensor: loading tensor blk.8.ssm_a
create_tensor: loading tensor blk.8.ssm_beta.weight
create_tensor: loading tensor blk.8.ssm_alpha.weight
create_tensor: loading tensor blk.8.ssm_norm.weight
create_tensor: loading tensor blk.8.ssm_out.weight
create_tensor: loading tensor blk.8.ffn_gate.weight
create_tensor: loading tensor blk.8.ffn_down.weight
create_tensor: loading tensor blk.8.ffn_up.weight
create_tensor: loading tensor blk.9.attn_norm.weight
create_tensor: loading tensor blk.9.post_attention_norm.weight
create_tensor: loading tensor blk.9.attn_qkv.weight
create_tensor: loading tensor blk.9.attn_gate.weight
create_tensor: loading tensor blk.9.ssm_conv1d.weight
create_tensor: loading tensor blk.9.ssm_dt.bias
create_tensor: loading tensor blk.9.ssm_a
create_tensor: loading tensor blk.9.ssm_beta.weight
create_tensor: loading tensor blk.9.ssm_alpha.weight
create_tensor: loading tensor blk.9.ssm_norm.weight
create_tensor: loading tensor blk.9.ssm_out.weight
create_tensor: loading tensor blk.9.ffn_gate.weight
create_tensor: loading tensor blk.9.ffn_down.weight
create_tensor: loading tensor blk.9.ffn_up.weight
create_tensor: loading tensor blk.10.attn_norm.weight
create_tensor: loading tensor blk.10.post_attention_norm.weight
create_tensor: loading tensor blk.10.attn_qkv.weight
create_tensor: loading tensor blk.10.attn_gate.weight
create_tensor: loading tensor blk.10.ssm_conv1d.weight
create_tensor: loading tensor blk.10.ssm_dt.bias
create_tensor: loading tensor blk.10.ssm_a
create_tensor: loading tensor blk.10.ssm_beta.weight
create_tensor: loading tensor blk.10.ssm_alpha.weight
create_tensor: loading tensor blk.10.ssm_norm.weight
create_tensor: loading tensor blk.10.ssm_out.weight
create_tensor: loading tensor blk.10.ffn_gate.weight
create_tensor: loading tensor blk.10.ffn_down.weight
create_tensor: loading tensor blk.10.ffn_up.weight
create_tensor: loading tensor blk.11.attn_norm.weight
create_tensor: loading tensor blk.11.post_attention_norm.weight
create_tensor: loading tensor blk.11.attn_q.weight
create_tensor: loading tensor blk.11.attn_k.weight
create_tensor: loading tensor blk.11.attn_v.weight
create_tensor: loading tensor blk.11.attn_output.weight
create_tensor: loading tensor blk.11.attn_q_norm.weight
create_tensor: loading tensor blk.11.attn_k_norm.weight
create_tensor: loading tensor blk.11.ffn_gate.weight
create_tensor: loading tensor blk.11.ffn_down.weight
create_tensor: loading tensor blk.11.ffn_up.weight
create_tensor: loading tensor blk.12.attn_norm.weight
create_tensor: loading tensor blk.12.post_attention_norm.weight
create_tensor: loading tensor blk.12.attn_qkv.weight
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create_tensor: loading tensor blk.12.ssm_conv1d.weight
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create_tensor: loading tensor blk.14.ssm_beta.weight
create_tensor: loading tensor blk.14.ssm_alpha.weight
create_tensor: loading tensor blk.14.ssm_norm.weight
create_tensor: loading tensor blk.14.ssm_out.weight
create_tensor: loading tensor blk.14.ffn_gate.weight
create_tensor: loading tensor blk.14.ffn_down.weight
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create_tensor: loading tensor blk.15.attn_norm.weight
create_tensor: loading tensor blk.15.post_attention_norm.weight
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create_tensor: loading tensor blk.15.attn_q_norm.weight
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create_tensor: loading tensor blk.15.ffn_gate.weight
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create_tensor: loading tensor blk.16.attn_norm.weight
create_tensor: loading tensor blk.16.post_attention_norm.weight
create_tensor: loading tensor blk.16.attn_qkv.weight
create_tensor: loading tensor blk.16.attn_gate.weight
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create_tensor: loading tensor blk.16.ssm_alpha.weight
create_tensor: loading tensor blk.16.ssm_norm.weight
create_tensor: loading tensor blk.16.ssm_out.weight
create_tensor: loading tensor blk.16.ffn_gate.weight
create_tensor: loading tensor blk.16.ffn_down.weight
create_tensor: loading tensor blk.16.ffn_up.weight
create_tensor: loading tensor blk.17.attn_norm.weight
create_tensor: loading tensor blk.17.post_attention_norm.weight
create_tensor: loading tensor blk.17.attn_qkv.weight
create_tensor: loading tensor blk.17.attn_gate.weight
create_tensor: loading tensor blk.17.ssm_conv1d.weight
create_tensor: loading tensor blk.17.ssm_dt.bias
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create_tensor: loading tensor blk.17.ssm_alpha.weight
create_tensor: loading tensor blk.17.ssm_norm.weight
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create_tensor: loading tensor blk.17.ffn_gate.weight
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create_tensor: loading tensor blk.18.attn_norm.weight
create_tensor: loading tensor blk.18.post_attention_norm.weight
create_tensor: loading tensor blk.18.attn_qkv.weight
create_tensor: loading tensor blk.18.attn_gate.weight
create_tensor: loading tensor blk.18.ssm_conv1d.weight
create_tensor: loading tensor blk.18.ssm_dt.bias
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create_tensor: loading tensor blk.18.ssm_norm.weight
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create_tensor: loading tensor blk.18.ffn_gate.weight
create_tensor: loading tensor blk.18.ffn_down.weight
create_tensor: loading tensor blk.18.ffn_up.weight
create_tensor: loading tensor blk.19.attn_norm.weight
create_tensor: loading tensor blk.19.post_attention_norm.weight
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create_tensor: loading tensor blk.19.attn_q_norm.weight
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create_tensor: loading tensor blk.19.ffn_gate.weight
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create_tensor: loading tensor blk.20.attn_norm.weight
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create_tensor: loading tensor blk.20.attn_qkv.weight
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create_tensor: loading tensor blk.20.ssm_norm.weight
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create_tensor: loading tensor blk.20.ffn_gate.weight
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create_tensor: loading tensor blk.21.attn_norm.weight
create_tensor: loading tensor blk.21.post_attention_norm.weight
create_tensor: loading tensor blk.21.attn_qkv.weight
create_tensor: loading tensor blk.21.attn_gate.weight
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create_tensor: loading tensor blk.21.ssm_dt.bias
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create_tensor: loading tensor blk.21.ssm_norm.weight
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create_tensor: loading tensor blk.21.ffn_gate.weight
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create_tensor: loading tensor blk.22.attn_norm.weight
create_tensor: loading tensor blk.22.post_attention_norm.weight
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create_tensor: loading tensor blk.23.attn_norm.weight
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create_tensor: loading tensor blk.24.attn_norm.weight
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create_tensor: loading tensor blk.25.attn_norm.weight
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create_tensor: loading tensor blk.26.attn_norm.weight
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create_tensor: loading tensor blk.27.attn_norm.weight
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create_tensor: loading tensor blk.27.ffn_gate.weight
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create_tensor: loading tensor blk.28.attn_norm.weight
create_tensor: loading tensor blk.28.post_attention_norm.weight
create_tensor: loading tensor blk.28.attn_qkv.weight
create_tensor: loading tensor blk.28.attn_gate.weight
create_tensor: loading tensor blk.28.ssm_conv1d.weight
create_tensor: loading tensor blk.28.ssm_dt.bias
create_tensor: loading tensor blk.28.ssm_a
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create_tensor: loading tensor blk.28.ffn_gate.weight
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create_tensor: loading tensor blk.29.attn_norm.weight
create_tensor: loading tensor blk.29.post_attention_norm.weight
create_tensor: loading tensor blk.29.attn_qkv.weight
create_tensor: loading tensor blk.29.attn_gate.weight
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create_tensor: loading tensor blk.29.ssm_dt.bias
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create_tensor: loading tensor blk.29.ssm_norm.weight
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create_tensor: loading tensor blk.29.ffn_gate.weight
create_tensor: loading tensor blk.29.ffn_down.weight
create_tensor: loading tensor blk.29.ffn_up.weight
create_tensor: loading tensor blk.30.attn_norm.weight
create_tensor: loading tensor blk.30.post_attention_norm.weight
create_tensor: loading tensor blk.30.attn_qkv.weight
create_tensor: loading tensor blk.30.attn_gate.weight
create_tensor: loading tensor blk.30.ssm_conv1d.weight
create_tensor: loading tensor blk.30.ssm_dt.bias
create_tensor: loading tensor blk.30.ssm_a
create_tensor: loading tensor blk.30.ssm_beta.weight
create_tensor: loading tensor blk.30.ssm_alpha.weight
create_tensor: loading tensor blk.30.ssm_norm.weight
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create_tensor: loading tensor blk.30.ffn_gate.weight
create_tensor: loading tensor blk.30.ffn_down.weight
create_tensor: loading tensor blk.30.ffn_up.weight
create_tensor: loading tensor blk.31.attn_norm.weight
create_tensor: loading tensor blk.31.post_attention_norm.weight
create_tensor: loading tensor blk.31.attn_q.weight
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create_tensor: loading tensor blk.31.attn_v.weight
create_tensor: loading tensor blk.31.attn_output.weight
create_tensor: loading tensor blk.31.attn_q_norm.weight
create_tensor: loading tensor blk.31.attn_k_norm.weight
create_tensor: loading tensor blk.31.ffn_gate.weight
create_tensor: loading tensor blk.31.ffn_down.weight
create_tensor: loading tensor blk.31.ffn_up.weight
done_getting_tensors: tensor 'token_embd.weight' (q6_K) (and 426 others) cannot be used with preferred buffer type CPU_REPACK, using CPU instead
load_tensors: offloading output layer to GPU
load_tensors: offloading 31 repeating layers to GPU
load_tensors: offloaded 33/33 layers to GPU
load_tensors:          CPU model buffer size =     0.00 MiB
llama_context: constructing llama_context
llama_context: n_seq_max     = 1
llama_context: n_ctx         = 4096
llama_context: n_ctx_seq     = 4096
llama_context: n_batch       = 2048
llama_context: n_ubatch      = 512
llama_context: causal_attn   = 1
llama_context: flash_attn    = enabled
llama_context: kv_unified    = false
llama_context: freq_base     = 10000000.0
llama_context: freq_scale    = 1
llama_context: n_ctx_seq (4096) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
set_abort_callback: call
llama_context:        CPU  output buffer size =     0.95 MiB
llama_kv_cache: layer   0: filtered
llama_kv_cache: layer   1: filtered
llama_kv_cache: layer   2: filtered
llama_kv_cache: layer   3: dev = CPU
llama_kv_cache: layer   4: filtered
llama_kv_cache: layer   5: filtered
llama_kv_cache: layer   6: filtered
llama_kv_cache: layer   7: dev = CPU
llama_kv_cache: layer   8: filtered
llama_kv_cache: layer   9: filtered
llama_kv_cache: layer  10: filtered
llama_kv_cache: layer  11: dev = CPU
llama_kv_cache: layer  12: filtered
llama_kv_cache: layer  13: filtered
llama_kv_cache: layer  14: filtered
llama_kv_cache: layer  15: dev = CPU
llama_kv_cache: layer  16: filtered
llama_kv_cache: layer  17: filtered
llama_kv_cache: layer  18: filtered
llama_kv_cache: layer  19: dev = CPU
llama_kv_cache: layer  20: filtered
llama_kv_cache: layer  21: filtered
llama_kv_cache: layer  22: filtered
llama_kv_cache: layer  23: dev = CPU
llama_kv_cache: layer  24: filtered
llama_kv_cache: layer  25: filtered
llama_kv_cache: layer  26: filtered
llama_kv_cache: layer  27: dev = CPU
llama_kv_cache: layer  28: filtered
llama_kv_cache: layer  29: filtered
llama_kv_cache: layer  30: filtered
llama_kv_cache: layer  31: dev = CPU
llama_kv_cache:        CPU KV buffer size =     0.00 MiB
llama_kv_cache: size =  128.00 MiB (  4096 cells,   8 layers,  1/1 seqs), K (f16):   64.00 MiB, V (f16):   64.00 MiB
llama_memory_recurrent, layer   0: dev = CPU
llama_memory_recurrent, layer   1: dev = CPU
llama_memory_recurrent, layer   2: dev = CPU
llama_memory_recurrent: layer   3: skipped
llama_memory_recurrent, layer   4: dev = CPU
llama_memory_recurrent, layer   5: dev = CPU
llama_memory_recurrent, layer   6: dev = CPU
llama_memory_recurrent: layer   7: skipped
llama_memory_recurrent, layer   8: dev = CPU
llama_memory_recurrent, layer   9: dev = CPU
llama_memory_recurrent, layer  10: dev = CPU
llama_memory_recurrent: layer  11: skipped
llama_memory_recurrent, layer  12: dev = CPU
llama_memory_recurrent, layer  13: dev = CPU
llama_memory_recurrent, layer  14: dev = CPU
llama_memory_recurrent: layer  15: skipped
llama_memory_recurrent, layer  16: dev = CPU
llama_memory_recurrent, layer  17: dev = CPU
llama_memory_recurrent, layer  18: dev = CPU
llama_memory_recurrent: layer  19: skipped
llama_memory_recurrent, layer  20: dev = CPU
llama_memory_recurrent, layer  21: dev = CPU
llama_memory_recurrent, layer  22: dev = CPU
llama_memory_recurrent: layer  23: skipped
llama_memory_recurrent, layer  24: dev = CPU
llama_memory_recurrent, layer  25: dev = CPU
llama_memory_recurrent, layer  26: dev = CPU
llama_memory_recurrent: layer  27: skipped
llama_memory_recurrent, layer  28: dev = CPU
llama_memory_recurrent, layer  29: dev = CPU
llama_memory_recurrent, layer  30: dev = CPU
llama_memory_recurrent: layer  31: skipped
llama_memory_recurrent:        CPU RS buffer size =    50.25 MiB
llama_memory_recurrent: size =   50.25 MiB (     1 cells,  32 layers,  1 seqs), R (f32):    2.25 MiB, S (f32):   48.00 MiB
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 1
sched_reserve: reserving ...
sched_reserve: max_nodes = 20480
sched_reserve: reserving full memory module
sched_reserve: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 1
sched_reserve: resolving fused Gated Delta Net support:
graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
sched_reserve: fused Gated Delta Net (autoregressive) enabled
graph_reserve: reserving a graph for ubatch with n_tokens =   16, n_seqs =  1, n_outputs =   16
sched_reserve: fused Gated Delta Net (chunked) enabled
graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
sched_reserve:        CPU compute buffer size =   495.00 MiB
sched_reserve: graph nodes  = 1833
sched_reserve: graph splits = 1
sched_reserve: reserve took 5.66 ms, sched copies = 1
llama_memory_breakdown_print: | memory breakdown [MiB] | total   free    self   model   context   compute    unaccounted |
llama_memory_breakdown_print: |   - Host               |                 3966 =  3293 +     178 +     495                |                                                                                                                 /llama_params_fit_impl: no devices with dedicated memory found
llama_params_fit: successfully fit params to free device memory
llama_params_fit: fitting params to free memory took 0.47 seconds
llama_model_loader: loaded meta data with 53 key-value pairs and 426 tensors from D:\Documents\Github\ai-backend-studio\models\Qwen3.5-4B-Abliterated-Claude-4.6-Opus-Reasoning-Distilled.i1-Q6_K.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = qwen35
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Qwen3.5-4B-Abliterated-Claude-4.6-Opu...
llama_model_loader: - kv   3:                            general.version str              = 4.6
llama_model_loader: - kv   4:                           general.finetune str              = Abliterated-Claude-Opus-Reasoning-Dis...
llama_model_loader: - kv   5:                           general.basename str              = Qwen3.5
llama_model_loader: - kv   6:                         general.size_label str              = 4B
llama_model_loader: - kv   7:                            general.license str              = other
llama_model_loader: - kv   8:                   general.base_model.count u32              = 1
llama_model_loader: - kv   9:                  general.base_model.0.name str              = Qwen3.5 4B Claude 4.6 Opus Reasoning ...
llama_model_loader: - kv  10:               general.base_model.0.version str              = 4.6
llama_model_loader: - kv  11:          general.base_model.0.organization str              = Jackrong
llama_model_loader: - kv  12:              general.base_model.0.repo_url str              = https://huggingface.co/Jackrong/Qwen3...
llama_model_loader: - kv  13:                               general.tags arr[str,5]       = ["abliterated", "de-censored", "reaso...
llama_model_loader: - kv  14:                         qwen35.block_count u32              = 32
llama_model_loader: - kv  15:                      qwen35.context_length u32              = 262144
llama_model_loader: - kv  16:                    qwen35.embedding_length u32              = 2560
llama_model_loader: - kv  17:                 qwen35.feed_forward_length u32              = 9216
llama_model_loader: - kv  18:                qwen35.attention.head_count u32              = 16
llama_model_loader: - kv  19:             qwen35.attention.head_count_kv u32              = 4
llama_model_loader: - kv  20:             qwen35.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
llama_model_loader: - kv  21:                      qwen35.rope.freq_base f32              = 10000000.000000
llama_model_loader: - kv  22:    qwen35.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  23:                qwen35.attention.key_length u32              = 256
llama_model_loader: - kv  24:              qwen35.attention.value_length u32              = 256
llama_model_loader: - kv  25:                     qwen35.ssm.conv_kernel u32              = 4
llama_model_loader: - kv  26:                      qwen35.ssm.state_size u32              = 128
llama_model_loader: - kv  27:                     qwen35.ssm.group_count u32              = 16
llama_model_loader: - kv  28:                  qwen35.ssm.time_step_rank u32              = 32
llama_model_loader: - kv  29:                      qwen35.ssm.inner_size u32              = 4096
llama_model_loader: - kv  30:             qwen35.full_attention_interval u32              = 4
llama_model_loader: - kv  31:                qwen35.rope.dimension_count u32              = 64
llama_model_loader: - kv  32:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  33:                         tokenizer.ggml.pre str              = qwen35                                                                                                                                         -llama_model_loader: - kv  34:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  35:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  36:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  37:                tokenizer.ggml.eos_token_id u32              = 248046
llama_model_loader: - kv  38:            tokenizer.ggml.padding_token_id u32              = 248055
llama_model_loader: - kv  39:                    tokenizer.chat_template str              = {%- if tools %}\n    {{- '<|im_start|>...
llama_model_loader: - kv  40:               general.quantization_version u32              = 2
llama_model_loader: - kv  41:                          general.file_type u32              = 18
llama_model_loader: - kv  42:                                general.url str              = https://huggingface.co/mradermacher/Q...
llama_model_loader: - kv  43:              mradermacher.quantize_version str              = 2
llama_model_loader: - kv  44:                  mradermacher.quantized_by str              = mradermacher
llama_model_loader: - kv  45:                  mradermacher.quantized_at str              = 2026-03-10T09:59:38+01:00
llama_model_loader: - kv  46:                  mradermacher.quantized_on str              = nico1
llama_model_loader: - kv  47:                         general.source.url str              = https://huggingface.co/Abhiray/Qwen3....
llama_model_loader: - kv  48:                  mradermacher.convert_type str              = hf
llama_model_loader: - kv  49:                      quantize.imatrix.file str              = Qwen3.5-4B-Abliterated-Claude-4.6-Opu...
llama_model_loader: - kv  50:                   quantize.imatrix.dataset str              = imatrix-training-full-3
llama_model_loader: - kv  51:             quantize.imatrix.entries_count u32              = 248
llama_model_loader: - kv  52:              quantize.imatrix.chunks_count u32              = 319
llama_model_loader: - type  f32:  177 tensors
llama_model_loader: - type q6_K:  249 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q6_K
print_info: file size   = 3.22 GiB (6.57 BPW)                                                                                                                                                                                              \init_tokenizer: initializing tokenizer for type 2
load: 0 unused tokens
load: control token: 248062 '<|fim_suffix|>' is not marked as EOG
load: control token: 248072 '<tts_pad>' is not marked as EOG
load: control token: 248050 '<|box_end|>' is not marked as EOG                                                                                                                                                                             |load: control token: 248048 '<|object_ref_end|>' is not marked as EOG
load: control token: 248055 '<|vision_pad|>' is not marked as EOG
load: control token: 248060 '<|fim_prefix|>' is not marked as EOG
load: control token: 248049 '<|box_start|>' is not marked as EOG
load: control token: 248073 '<tts_text_bos>' is not marked as EOG
load: control token: 248052 '<|quad_end|>' is not marked as EOG
load: control token: 248054 '<|vision_end|>' is not marked as EOG
load: control token: 248075 '<tts_text_bos_single>' is not marked as EOG
load: control token: 248056 '<|image_pad|>' is not marked as EOG
load: control token: 248071 '<|audio_end|>' is not marked as EOG
load: control token: 248045 '<|im_start|>' is not marked as EOG
load: control token: 248047 '<|object_ref_start|>' is not marked as EOG
load: control token: 248051 '<|quad_start|>' is not marked as EOG
load: control token: 248053 '<|vision_start|>' is not marked as EOG
load: control token: 248057 '<|video_pad|>' is not marked as EOG
load: control token: 248061 '<|fim_middle|>' is not marked as EOG
load: control token: 248070 '<|audio_start|>' is not marked as EOG
load: control token: 248074 '<tts_text_eod>' is not marked as EOG
load: control token: 248076 '<|audio_pad|>' is not marked as EOG
load: printing all EOG tokens:
load:   - 248044 ('<|endoftext|>')
load:   - 248046 ('<|im_end|>')
load:   - 248063 ('<|fim_pad|>')
load:   - 248064 ('<|repo_name|>')
load:   - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch                  = qwen35
print_info: vocab_only            = 0
print_info: no_alloc              = 0
print_info: n_ctx_train           = 262144
print_info: n_embd                = 2560
print_info: n_embd_inp            = 2560
print_info: n_layer               = 32
print_info: n_head                = 16
print_info: n_head_kv             = 4
print_info: n_rot                 = 64
print_info: n_swa                 = 0
print_info: is_swa_any            = 0
print_info: n_embd_head_k         = 256
print_info: n_embd_head_v         = 256
print_info: n_gqa                 = 4
print_info: n_embd_k_gqa          = 1024
print_info: n_embd_v_gqa          = 1024
print_info: f_norm_eps            = 0.0e+00
print_info: f_norm_rms_eps        = 1.0e-06
print_info: f_clamp_kqv           = 0.0e+00
print_info: f_max_alibi_bias      = 0.0e+00
print_info: f_logit_scale         = 0.0e+00
print_info: f_attn_scale          = 0.0e+00
print_info: n_ff                  = 9216
print_info: n_expert              = 0
print_info: n_expert_used         = 0
print_info: n_expert_groups       = 0
print_info: n_group_used          = 0
print_info: causal attn           = 1
print_info: pooling type          = -1
print_info: rope type             = 40
print_info: rope scaling          = linear
print_info: freq_base_train       = 10000000.0
print_info: freq_scale_train      = 1
print_info: n_ctx_orig_yarn       = 262144
print_info: rope_yarn_log_mul     = 0.0000
print_info: rope_finetuned        = unknown
print_info: mrope sections        = [11, 11, 10, 0]
print_info: ssm_d_conv            = 4
print_info: ssm_d_inner           = 4096
print_info: ssm_d_state           = 128
print_info: ssm_dt_rank           = 32
print_info: ssm_n_group           = 16
print_info: ssm_dt_b_c_rms        = 0
print_info: model type            = 4B
print_info: model params          = 4.21 B
print_info: general.name          = Qwen3.5-4B-Abliterated-Claude-4.6-Opus-Reasoning-Distilled
print_info: vocab type            = BPE
print_info: n_vocab               = 248320
print_info: n_merges              = 247587
print_info: BOS token             = 11 ','
print_info: EOS token             = 248046 '<|im_end|>'
print_info: EOT token             = 248046 '<|im_end|>'
print_info: PAD token             = 248055 '<|vision_pad|>'
print_info: LF token              = 198 'Ċ'
print_info: FIM PRE token         = 248060 '<|fim_prefix|>'
print_info: FIM SUF token         = 248062 '<|fim_suffix|>'
print_info: FIM MID token         = 248061 '<|fim_middle|>'
print_info: FIM PAD token         = 248063 '<|fim_pad|>'
print_info: FIM REP token         = 248064 '<|repo_name|>'
print_info: FIM SEP token         = 248065 '<|file_sep|>'
print_info: EOG token             = 248044 '<|endoftext|>'
print_info: EOG token             = 248046 '<|im_end|>'
print_info: EOG token             = 248063 '<|fim_pad|>'
print_info: EOG token             = 248064 '<|repo_name|>'
print_info: EOG token             = 248065 '<|file_sep|>'
print_info: max token length      = 256
load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
load_tensors: layer   0 assigned to device CPU, is_swa = 0
load_tensors: layer   1 assigned to device CPU, is_swa = 0
load_tensors: layer   2 assigned to device CPU, is_swa = 0
load_tensors: layer   3 assigned to device CPU, is_swa = 0
load_tensors: layer   4 assigned to device CPU, is_swa = 0
load_tensors: layer   5 assigned to device CPU, is_swa = 0
load_tensors: layer   6 assigned to device CPU, is_swa = 0
load_tensors: layer   7 assigned to device CPU, is_swa = 0
load_tensors: layer   8 assigned to device CPU, is_swa = 0
load_tensors: layer   9 assigned to device CPU, is_swa = 0
load_tensors: layer  10 assigned to device CPU, is_swa = 0
load_tensors: layer  11 assigned to device CPU, is_swa = 0
load_tensors: layer  12 assigned to device CPU, is_swa = 0
load_tensors: layer  13 assigned to device CPU, is_swa = 0
load_tensors: layer  14 assigned to device CPU, is_swa = 0
load_tensors: layer  15 assigned to device CPU, is_swa = 0
load_tensors: layer  16 assigned to device CPU, is_swa = 0
load_tensors: layer  17 assigned to device CPU, is_swa = 0
load_tensors: layer  18 assigned to device CPU, is_swa = 0
load_tensors: layer  19 assigned to device CPU, is_swa = 0
load_tensors: layer  20 assigned to device CPU, is_swa = 0
load_tensors: layer  21 assigned to device CPU, is_swa = 0
load_tensors: layer  22 assigned to device CPU, is_swa = 0
load_tensors: layer  23 assigned to device CPU, is_swa = 0
load_tensors: layer  24 assigned to device CPU, is_swa = 0
load_tensors: layer  25 assigned to device CPU, is_swa = 0
load_tensors: layer  26 assigned to device CPU, is_swa = 0
load_tensors: layer  27 assigned to device CPU, is_swa = 0
load_tensors: layer  28 assigned to device CPU, is_swa = 0
load_tensors: layer  29 assigned to device CPU, is_swa = 0
load_tensors: layer  30 assigned to device CPU, is_swa = 0
load_tensors: layer  31 assigned to device CPU, is_swa = 0
load_tensors: layer  32 assigned to device CPU, is_swa = 0
create_tensor: loading tensor token_embd.weight
create_tensor: loading tensor output_norm.weight
create_tensor: loading tensor blk.0.attn_norm.weight
create_tensor: loading tensor blk.0.post_attention_norm.weight
create_tensor: loading tensor blk.0.attn_qkv.weight
create_tensor: loading tensor blk.0.attn_gate.weight
create_tensor: loading tensor blk.0.ssm_conv1d.weight
create_tensor: loading tensor blk.0.ssm_dt.bias
create_tensor: loading tensor blk.0.ssm_a
create_tensor: loading tensor blk.0.ssm_beta.weight
create_tensor: loading tensor blk.0.ssm_alpha.weight
create_tensor: loading tensor blk.0.ssm_norm.weight
create_tensor: loading tensor blk.0.ssm_out.weight
create_tensor: loading tensor blk.0.ffn_gate.weight
create_tensor: loading tensor blk.0.ffn_down.weight
create_tensor: loading tensor blk.0.ffn_up.weight
create_tensor: loading tensor blk.1.attn_norm.weight
create_tensor: loading tensor blk.1.post_attention_norm.weight
create_tensor: loading tensor blk.1.attn_qkv.weight
create_tensor: loading tensor blk.1.attn_gate.weight
create_tensor: loading tensor blk.1.ssm_conv1d.weight
create_tensor: loading tensor blk.1.ssm_dt.bias
create_tensor: loading tensor blk.1.ssm_a
create_tensor: loading tensor blk.1.ssm_beta.weight
create_tensor: loading tensor blk.1.ssm_alpha.weight
create_tensor: loading tensor blk.1.ssm_norm.weight
create_tensor: loading tensor blk.1.ssm_out.weight
create_tensor: loading tensor blk.1.ffn_gate.weight
create_tensor: loading tensor blk.1.ffn_down.weight
create_tensor: loading tensor blk.1.ffn_up.weight
create_tensor: loading tensor blk.2.attn_norm.weight
create_tensor: loading tensor blk.2.post_attention_norm.weight
create_tensor: loading tensor blk.2.attn_qkv.weight
create_tensor: loading tensor blk.2.attn_gate.weight
create_tensor: loading tensor blk.2.ssm_conv1d.weight
create_tensor: loading tensor blk.2.ssm_dt.bias
create_tensor: loading tensor blk.2.ssm_a
create_tensor: loading tensor blk.2.ssm_beta.weight
create_tensor: loading tensor blk.2.ssm_alpha.weight
create_tensor: loading tensor blk.2.ssm_norm.weight
create_tensor: loading tensor blk.2.ssm_out.weight
create_tensor: loading tensor blk.2.ffn_gate.weight
create_tensor: loading tensor blk.2.ffn_down.weight
create_tensor: loading tensor blk.2.ffn_up.weight
create_tensor: loading tensor blk.3.attn_norm.weight
create_tensor: loading tensor blk.3.post_attention_norm.weight
create_tensor: loading tensor blk.3.attn_q.weight
create_tensor: loading tensor blk.3.attn_k.weight
create_tensor: loading tensor blk.3.attn_v.weight
create_tensor: loading tensor blk.3.attn_output.weight
create_tensor: loading tensor blk.3.attn_q_norm.weight
create_tensor: loading tensor blk.3.attn_k_norm.weight
create_tensor: loading tensor blk.3.ffn_gate.weight
create_tensor: loading tensor blk.3.ffn_down.weight
create_tensor: loading tensor blk.3.ffn_up.weight
create_tensor: loading tensor blk.4.attn_norm.weight
create_tensor: loading tensor blk.4.post_attention_norm.weight
create_tensor: loading tensor blk.4.attn_qkv.weight
create_tensor: loading tensor blk.4.attn_gate.weight
create_tensor: loading tensor blk.4.ssm_conv1d.weight
create_tensor: loading tensor blk.4.ssm_dt.bias
create_tensor: loading tensor blk.4.ssm_a
create_tensor: loading tensor blk.4.ssm_beta.weight
create_tensor: loading tensor blk.4.ssm_alpha.weight
create_tensor: loading tensor blk.4.ssm_norm.weight
create_tensor: loading tensor blk.4.ssm_out.weight
create_tensor: loading tensor blk.4.ffn_gate.weight
create_tensor: loading tensor blk.4.ffn_down.weight
create_tensor: loading tensor blk.4.ffn_up.weight
create_tensor: loading tensor blk.5.attn_norm.weight
create_tensor: loading tensor blk.5.post_attention_norm.weight
create_tensor: loading tensor blk.5.attn_qkv.weight
create_tensor: loading tensor blk.5.attn_gate.weight
create_tensor: loading tensor blk.5.ssm_conv1d.weight
create_tensor: loading tensor blk.5.ssm_dt.bias
create_tensor: loading tensor blk.5.ssm_a
create_tensor: loading tensor blk.5.ssm_beta.weight
create_tensor: loading tensor blk.5.ssm_alpha.weight
create_tensor: loading tensor blk.5.ssm_norm.weight
create_tensor: loading tensor blk.5.ssm_out.weight
create_tensor: loading tensor blk.5.ffn_gate.weight
create_tensor: loading tensor blk.5.ffn_down.weight
create_tensor: loading tensor blk.5.ffn_up.weight
create_tensor: loading tensor blk.6.attn_norm.weight
create_tensor: loading tensor blk.6.post_attention_norm.weight
create_tensor: loading tensor blk.6.attn_qkv.weight
create_tensor: loading tensor blk.6.attn_gate.weight
create_tensor: loading tensor blk.6.ssm_conv1d.weight
create_tensor: loading tensor blk.6.ssm_dt.bias
create_tensor: loading tensor blk.6.ssm_a
create_tensor: loading tensor blk.6.ssm_beta.weight
create_tensor: loading tensor blk.6.ssm_alpha.weight
create_tensor: loading tensor blk.6.ssm_norm.weight
create_tensor: loading tensor blk.6.ssm_out.weight
create_tensor: loading tensor blk.6.ffn_gate.weight
create_tensor: loading tensor blk.6.ffn_down.weight
create_tensor: loading tensor blk.6.ffn_up.weight
create_tensor: loading tensor blk.7.attn_norm.weight
create_tensor: loading tensor blk.7.post_attention_norm.weight
create_tensor: loading tensor blk.7.attn_q.weight
create_tensor: loading tensor blk.7.attn_k.weight
create_tensor: loading tensor blk.7.attn_v.weight
create_tensor: loading tensor blk.7.attn_output.weight
create_tensor: loading tensor blk.7.attn_q_norm.weight
create_tensor: loading tensor blk.7.attn_k_norm.weight
create_tensor: loading tensor blk.7.ffn_gate.weight
create_tensor: loading tensor blk.7.ffn_down.weight
create_tensor: loading tensor blk.7.ffn_up.weight
create_tensor: loading tensor blk.8.attn_norm.weight
create_tensor: loading tensor blk.8.post_attention_norm.weight
create_tensor: loading tensor blk.8.attn_qkv.weight
create_tensor: loading tensor blk.8.attn_gate.weight
create_tensor: loading tensor blk.8.ssm_conv1d.weight
create_tensor: loading tensor blk.8.ssm_dt.bias
create_tensor: loading tensor blk.8.ssm_a
create_tensor: loading tensor blk.8.ssm_beta.weight
create_tensor: loading tensor blk.8.ssm_alpha.weight
create_tensor: loading tensor blk.8.ssm_norm.weight
create_tensor: loading tensor blk.8.ssm_out.weight
create_tensor: loading tensor blk.8.ffn_gate.weight
create_tensor: loading tensor blk.8.ffn_down.weight
create_tensor: loading tensor blk.8.ffn_up.weight
create_tensor: loading tensor blk.9.attn_norm.weight
create_tensor: loading tensor blk.9.post_attention_norm.weight
create_tensor: loading tensor blk.9.attn_qkv.weight
create_tensor: loading tensor blk.9.attn_gate.weight
create_tensor: loading tensor blk.9.ssm_conv1d.weight
create_tensor: loading tensor blk.9.ssm_dt.bias
create_tensor: loading tensor blk.9.ssm_a
create_tensor: loading tensor blk.9.ssm_beta.weight
create_tensor: loading tensor blk.9.ssm_alpha.weight
create_tensor: loading tensor blk.9.ssm_norm.weight
create_tensor: loading tensor blk.9.ssm_out.weight
create_tensor: loading tensor blk.9.ffn_gate.weight
create_tensor: loading tensor blk.9.ffn_down.weight
create_tensor: loading tensor blk.9.ffn_up.weight
create_tensor: loading tensor blk.10.attn_norm.weight
create_tensor: loading tensor blk.10.post_attention_norm.weight
create_tensor: loading tensor blk.10.attn_qkv.weight
create_tensor: loading tensor blk.10.attn_gate.weight
create_tensor: loading tensor blk.10.ssm_conv1d.weight
create_tensor: loading tensor blk.10.ssm_dt.bias
create_tensor: loading tensor blk.10.ssm_a
create_tensor: loading tensor blk.10.ssm_beta.weight
create_tensor: loading tensor blk.10.ssm_alpha.weight
create_tensor: loading tensor blk.10.ssm_norm.weight
create_tensor: loading tensor blk.10.ssm_out.weight
create_tensor: loading tensor blk.10.ffn_gate.weight
create_tensor: loading tensor blk.10.ffn_down.weight
create_tensor: loading tensor blk.10.ffn_up.weight
create_tensor: loading tensor blk.11.attn_norm.weight
create_tensor: loading tensor blk.11.post_attention_norm.weight
create_tensor: loading tensor blk.11.attn_q.weight
create_tensor: loading tensor blk.11.attn_k.weight
create_tensor: loading tensor blk.11.attn_v.weight
create_tensor: loading tensor blk.11.attn_output.weight
create_tensor: loading tensor blk.11.attn_q_norm.weight
create_tensor: loading tensor blk.11.attn_k_norm.weight
create_tensor: loading tensor blk.11.ffn_gate.weight
create_tensor: loading tensor blk.11.ffn_down.weight
create_tensor: loading tensor blk.11.ffn_up.weight
create_tensor: loading tensor blk.12.attn_norm.weight
create_tensor: loading tensor blk.12.post_attention_norm.weight
create_tensor: loading tensor blk.12.attn_qkv.weight
create_tensor: loading tensor blk.12.attn_gate.weight
create_tensor: loading tensor blk.12.ssm_conv1d.weight
create_tensor: loading tensor blk.12.ssm_dt.bias
create_tensor: loading tensor blk.12.ssm_a
create_tensor: loading tensor blk.12.ssm_beta.weight
create_tensor: loading tensor blk.12.ssm_alpha.weight
create_tensor: loading tensor blk.12.ssm_norm.weight
create_tensor: loading tensor blk.12.ssm_out.weight
create_tensor: loading tensor blk.12.ffn_gate.weight
create_tensor: loading tensor blk.12.ffn_down.weight
create_tensor: loading tensor blk.12.ffn_up.weight
create_tensor: loading tensor blk.13.attn_norm.weight
create_tensor: loading tensor blk.13.post_attention_norm.weight
create_tensor: loading tensor blk.13.attn_qkv.weight
create_tensor: loading tensor blk.13.attn_gate.weight
create_tensor: loading tensor blk.13.ssm_conv1d.weight
create_tensor: loading tensor blk.13.ssm_dt.bias
create_tensor: loading tensor blk.13.ssm_a
create_tensor: loading tensor blk.13.ssm_beta.weight
create_tensor: loading tensor blk.13.ssm_alpha.weight
create_tensor: loading tensor blk.13.ssm_norm.weight
create_tensor: loading tensor blk.13.ssm_out.weight
create_tensor: loading tensor blk.13.ffn_gate.weight
create_tensor: loading tensor blk.13.ffn_down.weight
create_tensor: loading tensor blk.13.ffn_up.weight
create_tensor: loading tensor blk.14.attn_norm.weight
create_tensor: loading tensor blk.14.post_attention_norm.weight
create_tensor: loading tensor blk.14.attn_qkv.weight
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create_tensor: loading tensor blk.14.ssm_conv1d.weight
create_tensor: loading tensor blk.14.ssm_dt.bias
create_tensor: loading tensor blk.14.ssm_a
create_tensor: loading tensor blk.14.ssm_beta.weight
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create_tensor: loading tensor blk.14.ssm_norm.weight
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create_tensor: loading tensor blk.14.ffn_gate.weight
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create_tensor: loading tensor blk.15.attn_norm.weight
create_tensor: loading tensor blk.15.post_attention_norm.weight
create_tensor: loading tensor blk.15.attn_q.weight
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create_tensor: loading tensor blk.15.attn_v.weight
create_tensor: loading tensor blk.15.attn_output.weight
create_tensor: loading tensor blk.15.attn_q_norm.weight
create_tensor: loading tensor blk.15.attn_k_norm.weight
create_tensor: loading tensor blk.15.ffn_gate.weight
create_tensor: loading tensor blk.15.ffn_down.weight
create_tensor: loading tensor blk.15.ffn_up.weight
create_tensor: loading tensor blk.16.attn_norm.weight
create_tensor: loading tensor blk.16.post_attention_norm.weight
create_tensor: loading tensor blk.16.attn_qkv.weight
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create_tensor: loading tensor blk.16.ssm_conv1d.weight
create_tensor: loading tensor blk.16.ssm_dt.bias
create_tensor: loading tensor blk.16.ssm_a
create_tensor: loading tensor blk.16.ssm_beta.weight
create_tensor: loading tensor blk.16.ssm_alpha.weight
create_tensor: loading tensor blk.16.ssm_norm.weight
create_tensor: loading tensor blk.16.ssm_out.weight
create_tensor: loading tensor blk.16.ffn_gate.weight
create_tensor: loading tensor blk.16.ffn_down.weight
create_tensor: loading tensor blk.16.ffn_up.weight
create_tensor: loading tensor blk.17.attn_norm.weight
create_tensor: loading tensor blk.17.post_attention_norm.weight
create_tensor: loading tensor blk.17.attn_qkv.weight
create_tensor: loading tensor blk.17.attn_gate.weight
create_tensor: loading tensor blk.17.ssm_conv1d.weight
create_tensor: loading tensor blk.17.ssm_dt.bias
create_tensor: loading tensor blk.17.ssm_a
create_tensor: loading tensor blk.17.ssm_beta.weight
create_tensor: loading tensor blk.17.ssm_alpha.weight
create_tensor: loading tensor blk.17.ssm_norm.weight
create_tensor: loading tensor blk.17.ssm_out.weight
create_tensor: loading tensor blk.17.ffn_gate.weight
create_tensor: loading tensor blk.17.ffn_down.weight
create_tensor: loading tensor blk.17.ffn_up.weight
create_tensor: loading tensor blk.18.attn_norm.weight
create_tensor: loading tensor blk.18.post_attention_norm.weight
create_tensor: loading tensor blk.18.attn_qkv.weight
create_tensor: loading tensor blk.18.attn_gate.weight
create_tensor: loading tensor blk.18.ssm_conv1d.weight
create_tensor: loading tensor blk.18.ssm_dt.bias
create_tensor: loading tensor blk.18.ssm_a
create_tensor: loading tensor blk.18.ssm_beta.weight
create_tensor: loading tensor blk.18.ssm_alpha.weight
create_tensor: loading tensor blk.18.ssm_norm.weight
create_tensor: loading tensor blk.18.ssm_out.weight
create_tensor: loading tensor blk.18.ffn_gate.weight
create_tensor: loading tensor blk.18.ffn_down.weight
create_tensor: loading tensor blk.18.ffn_up.weight
create_tensor: loading tensor blk.19.attn_norm.weight
create_tensor: loading tensor blk.19.post_attention_norm.weight
create_tensor: loading tensor blk.19.attn_q.weight
create_tensor: loading tensor blk.19.attn_k.weight
create_tensor: loading tensor blk.19.attn_v.weight
create_tensor: loading tensor blk.19.attn_output.weight
create_tensor: loading tensor blk.19.attn_q_norm.weight
create_tensor: loading tensor blk.19.attn_k_norm.weight
create_tensor: loading tensor blk.19.ffn_gate.weight
create_tensor: loading tensor blk.19.ffn_down.weight
create_tensor: loading tensor blk.19.ffn_up.weight
create_tensor: loading tensor blk.20.attn_norm.weight
create_tensor: loading tensor blk.20.post_attention_norm.weight
create_tensor: loading tensor blk.20.attn_qkv.weight
create_tensor: loading tensor blk.20.attn_gate.weight
create_tensor: loading tensor blk.20.ssm_conv1d.weight
create_tensor: loading tensor blk.20.ssm_dt.bias
create_tensor: loading tensor blk.20.ssm_a
create_tensor: loading tensor blk.20.ssm_beta.weight
create_tensor: loading tensor blk.20.ssm_alpha.weight
create_tensor: loading tensor blk.20.ssm_norm.weight
create_tensor: loading tensor blk.20.ssm_out.weight
create_tensor: loading tensor blk.20.ffn_gate.weight
create_tensor: loading tensor blk.20.ffn_down.weight
create_tensor: loading tensor blk.20.ffn_up.weight
create_tensor: loading tensor blk.21.attn_norm.weight
create_tensor: loading tensor blk.21.post_attention_norm.weight
create_tensor: loading tensor blk.21.attn_qkv.weight
create_tensor: loading tensor blk.21.attn_gate.weight
create_tensor: loading tensor blk.21.ssm_conv1d.weight
create_tensor: loading tensor blk.21.ssm_dt.bias
create_tensor: loading tensor blk.21.ssm_a
create_tensor: loading tensor blk.21.ssm_beta.weight
create_tensor: loading tensor blk.21.ssm_alpha.weight
create_tensor: loading tensor blk.21.ssm_norm.weight
create_tensor: loading tensor blk.21.ssm_out.weight
create_tensor: loading tensor blk.21.ffn_gate.weight
create_tensor: loading tensor blk.21.ffn_down.weight
create_tensor: loading tensor blk.21.ffn_up.weight
create_tensor: loading tensor blk.22.attn_norm.weight
create_tensor: loading tensor blk.22.post_attention_norm.weight
create_tensor: loading tensor blk.22.attn_qkv.weight
create_tensor: loading tensor blk.22.attn_gate.weight
create_tensor: loading tensor blk.22.ssm_conv1d.weight
create_tensor: loading tensor blk.22.ssm_dt.bias
create_tensor: loading tensor blk.22.ssm_a
create_tensor: loading tensor blk.22.ssm_beta.weight
create_tensor: loading tensor blk.22.ssm_alpha.weight
create_tensor: loading tensor blk.22.ssm_norm.weight
create_tensor: loading tensor blk.22.ssm_out.weight
create_tensor: loading tensor blk.22.ffn_gate.weight
create_tensor: loading tensor blk.22.ffn_down.weight
create_tensor: loading tensor blk.22.ffn_up.weight
create_tensor: loading tensor blk.23.attn_norm.weight
create_tensor: loading tensor blk.23.post_attention_norm.weight
create_tensor: loading tensor blk.23.attn_q.weight
create_tensor: loading tensor blk.23.attn_k.weight
create_tensor: loading tensor blk.23.attn_v.weight
create_tensor: loading tensor blk.23.attn_output.weight
create_tensor: loading tensor blk.23.attn_q_norm.weight
create_tensor: loading tensor blk.23.attn_k_norm.weight
create_tensor: loading tensor blk.23.ffn_gate.weight
create_tensor: loading tensor blk.23.ffn_down.weight
create_tensor: loading tensor blk.23.ffn_up.weight
create_tensor: loading tensor blk.24.attn_norm.weight
create_tensor: loading tensor blk.24.post_attention_norm.weight
create_tensor: loading tensor blk.24.attn_qkv.weight
create_tensor: loading tensor blk.24.attn_gate.weight
create_tensor: loading tensor blk.24.ssm_conv1d.weight
create_tensor: loading tensor blk.24.ssm_dt.bias
create_tensor: loading tensor blk.24.ssm_a
create_tensor: loading tensor blk.24.ssm_beta.weight
create_tensor: loading tensor blk.24.ssm_alpha.weight
create_tensor: loading tensor blk.24.ssm_norm.weight
create_tensor: loading tensor blk.24.ssm_out.weight
create_tensor: loading tensor blk.24.ffn_gate.weight
create_tensor: loading tensor blk.24.ffn_down.weight
create_tensor: loading tensor blk.24.ffn_up.weight
create_tensor: loading tensor blk.25.attn_norm.weight
create_tensor: loading tensor blk.25.post_attention_norm.weight
create_tensor: loading tensor blk.25.attn_qkv.weight
create_tensor: loading tensor blk.25.attn_gate.weight
create_tensor: loading tensor blk.25.ssm_conv1d.weight
create_tensor: loading tensor blk.25.ssm_dt.bias
create_tensor: loading tensor blk.25.ssm_a
create_tensor: loading tensor blk.25.ssm_beta.weight
create_tensor: loading tensor blk.25.ssm_alpha.weight
create_tensor: loading tensor blk.25.ssm_norm.weight
create_tensor: loading tensor blk.25.ssm_out.weight
create_tensor: loading tensor blk.25.ffn_gate.weight
create_tensor: loading tensor blk.25.ffn_down.weight
create_tensor: loading tensor blk.25.ffn_up.weight
create_tensor: loading tensor blk.26.attn_norm.weight
create_tensor: loading tensor blk.26.post_attention_norm.weight
create_tensor: loading tensor blk.26.attn_qkv.weight
create_tensor: loading tensor blk.26.attn_gate.weight
create_tensor: loading tensor blk.26.ssm_conv1d.weight
create_tensor: loading tensor blk.26.ssm_dt.bias
create_tensor: loading tensor blk.26.ssm_a
create_tensor: loading tensor blk.26.ssm_beta.weight
create_tensor: loading tensor blk.26.ssm_alpha.weight
create_tensor: loading tensor blk.26.ssm_norm.weight
create_tensor: loading tensor blk.26.ssm_out.weight
create_tensor: loading tensor blk.26.ffn_gate.weight
create_tensor: loading tensor blk.26.ffn_down.weight
create_tensor: loading tensor blk.26.ffn_up.weight
create_tensor: loading tensor blk.27.attn_norm.weight
create_tensor: loading tensor blk.27.post_attention_norm.weight
create_tensor: loading tensor blk.27.attn_q.weight
create_tensor: loading tensor blk.27.attn_k.weight
create_tensor: loading tensor blk.27.attn_v.weight
create_tensor: loading tensor blk.27.attn_output.weight
create_tensor: loading tensor blk.27.attn_q_norm.weight
create_tensor: loading tensor blk.27.attn_k_norm.weight
create_tensor: loading tensor blk.27.ffn_gate.weight
create_tensor: loading tensor blk.27.ffn_down.weight
create_tensor: loading tensor blk.27.ffn_up.weight
create_tensor: loading tensor blk.28.attn_norm.weight
create_tensor: loading tensor blk.28.post_attention_norm.weight
create_tensor: loading tensor blk.28.attn_qkv.weight
create_tensor: loading tensor blk.28.attn_gate.weight
create_tensor: loading tensor blk.28.ssm_conv1d.weight
create_tensor: loading tensor blk.28.ssm_dt.bias
create_tensor: loading tensor blk.28.ssm_a
create_tensor: loading tensor blk.28.ssm_beta.weight
create_tensor: loading tensor blk.28.ssm_alpha.weight
create_tensor: loading tensor blk.28.ssm_norm.weight
create_tensor: loading tensor blk.28.ssm_out.weight
create_tensor: loading tensor blk.28.ffn_gate.weight
create_tensor: loading tensor blk.28.ffn_down.weight
create_tensor: loading tensor blk.28.ffn_up.weight
create_tensor: loading tensor blk.29.attn_norm.weight
create_tensor: loading tensor blk.29.post_attention_norm.weight
create_tensor: loading tensor blk.29.attn_qkv.weight
create_tensor: loading tensor blk.29.attn_gate.weight
create_tensor: loading tensor blk.29.ssm_conv1d.weight
create_tensor: loading tensor blk.29.ssm_dt.bias
create_tensor: loading tensor blk.29.ssm_a
create_tensor: loading tensor blk.29.ssm_beta.weight
create_tensor: loading tensor blk.29.ssm_alpha.weight
create_tensor: loading tensor blk.29.ssm_norm.weight
create_tensor: loading tensor blk.29.ssm_out.weight
create_tensor: loading tensor blk.29.ffn_gate.weight
create_tensor: loading tensor blk.29.ffn_down.weight
create_tensor: loading tensor blk.29.ffn_up.weight
create_tensor: loading tensor blk.30.attn_norm.weight
create_tensor: loading tensor blk.30.post_attention_norm.weight
create_tensor: loading tensor blk.30.attn_qkv.weight
create_tensor: loading tensor blk.30.attn_gate.weight
create_tensor: loading tensor blk.30.ssm_conv1d.weight
create_tensor: loading tensor blk.30.ssm_dt.bias
create_tensor: loading tensor blk.30.ssm_a
create_tensor: loading tensor blk.30.ssm_beta.weight
create_tensor: loading tensor blk.30.ssm_alpha.weight
create_tensor: loading tensor blk.30.ssm_norm.weight
create_tensor: loading tensor blk.30.ssm_out.weight
create_tensor: loading tensor blk.30.ffn_gate.weight
create_tensor: loading tensor blk.30.ffn_down.weight
create_tensor: loading tensor blk.30.ffn_up.weight
create_tensor: loading tensor blk.31.attn_norm.weight
create_tensor: loading tensor blk.31.post_attention_norm.weight
create_tensor: loading tensor blk.31.attn_q.weight
create_tensor: loading tensor blk.31.attn_k.weight
create_tensor: loading tensor blk.31.attn_v.weight
create_tensor: loading tensor blk.31.attn_output.weight
create_tensor: loading tensor blk.31.attn_q_norm.weight
create_tensor: loading tensor blk.31.attn_k_norm.weight
create_tensor: loading tensor blk.31.ffn_gate.weight
create_tensor: loading tensor blk.31.ffn_down.weight
create_tensor: loading tensor blk.31.ffn_up.weight
done_getting_tensors: tensor 'token_embd.weight' (q6_K) (and 426 others) cannot be used with preferred buffer type CPU_REPACK, using CPU instead                                                                                           -load_tensors: offloading output layer to GPU
load_tensors: offloading 31 repeating layers to GPU
load_tensors: offloaded 33/33 layers to GPU
load_tensors:   CPU_Mapped model buffer size =  3293.12 MiB
......................................................................................
common_init_result: added <|endoftext|> logit bias = -inf
common_init_result: added <|im_end|> logit bias = -inf
common_init_result: added <|fim_pad|> logit bias = -inf
common_init_result: added <|repo_name|> logit bias = -inf
common_init_result: added <|file_sep|> logit bias = -inf
llama_context: constructing llama_context
llama_context: n_seq_max     = 1
llama_context: n_ctx         = 4096
llama_context: n_ctx_seq     = 4096
llama_context: n_batch       = 2048
llama_context: n_ubatch      = 512
llama_context: causal_attn   = 1
llama_context: flash_attn    = enabled
llama_context: kv_unified    = false
llama_context: freq_base     = 10000000.0
llama_context: freq_scale    = 1
llama_context: n_ctx_seq (4096) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
set_abort_callback: call
llama_context:        CPU  output buffer size =     0.95 MiB
llama_kv_cache: layer   0: filtered
llama_kv_cache: layer   1: filtered
llama_kv_cache: layer   2: filtered
llama_kv_cache: layer   3: dev = CPU
llama_kv_cache: layer   4: filtered
llama_kv_cache: layer   5: filtered
llama_kv_cache: layer   6: filtered
llama_kv_cache: layer   7: dev = CPU
llama_kv_cache: layer   8: filtered
llama_kv_cache: layer   9: filtered
llama_kv_cache: layer  10: filtered
llama_kv_cache: layer  11: dev = CPU
llama_kv_cache: layer  12: filtered
llama_kv_cache: layer  13: filtered
llama_kv_cache: layer  14: filtered
llama_kv_cache: layer  15: dev = CPU
llama_kv_cache: layer  16: filtered
llama_kv_cache: layer  17: filtered
llama_kv_cache: layer  18: filtered
llama_kv_cache: layer  19: dev = CPU
llama_kv_cache: layer  20: filtered
llama_kv_cache: layer  21: filtered
llama_kv_cache: layer  22: filtered
llama_kv_cache: layer  23: dev = CPU
llama_kv_cache: layer  24: filtered
llama_kv_cache: layer  25: filtered
llama_kv_cache: layer  26: filtered
llama_kv_cache: layer  27: dev = CPU
llama_kv_cache: layer  28: filtered
llama_kv_cache: layer  29: filtered
llama_kv_cache: layer  30: filtered
llama_kv_cache: layer  31: dev = CPU
llama_kv_cache:        CPU KV buffer size =   128.00 MiB                                                                                                                                                                                   \llama_kv_cache: size =  128.00 MiB (  4096 cells,   8 layers,  1/1 seqs), K (f16):   64.00 MiB, V (f16):   64.00 MiB
llama_memory_recurrent, layer   0: dev = CPU
llama_memory_recurrent, layer   1: dev = CPU
llama_memory_recurrent, layer   2: dev = CPU
llama_memory_recurrent: layer   3: skipped
llama_memory_recurrent, layer   4: dev = CPU
llama_memory_recurrent, layer   5: dev = CPU
llama_memory_recurrent, layer   6: dev = CPU
llama_memory_recurrent: layer   7: skipped
llama_memory_recurrent, layer   8: dev = CPU
llama_memory_recurrent, layer   9: dev = CPU
llama_memory_recurrent, layer  10: dev = CPU
llama_memory_recurrent: layer  11: skipped
llama_memory_recurrent, layer  12: dev = CPU
llama_memory_recurrent, layer  13: dev = CPU
llama_memory_recurrent, layer  14: dev = CPU
llama_memory_recurrent: layer  15: skipped
llama_memory_recurrent, layer  16: dev = CPU
llama_memory_recurrent, layer  17: dev = CPU
llama_memory_recurrent, layer  18: dev = CPU
llama_memory_recurrent: layer  19: skipped
llama_memory_recurrent, layer  20: dev = CPU
llama_memory_recurrent, layer  21: dev = CPU
llama_memory_recurrent, layer  22: dev = CPU
llama_memory_recurrent: layer  23: skipped
llama_memory_recurrent, layer  24: dev = CPU
llama_memory_recurrent, layer  25: dev = CPU
llama_memory_recurrent, layer  26: dev = CPU
llama_memory_recurrent: layer  27: skipped
llama_memory_recurrent, layer  28: dev = CPU
llama_memory_recurrent, layer  29: dev = CPU
llama_memory_recurrent, layer  30: dev = CPU
llama_memory_recurrent: layer  31: skipped
llama_memory_recurrent:        CPU RS buffer size =    50.25 MiB
llama_memory_recurrent: size =   50.25 MiB (     1 cells,  32 layers,  1 seqs), R (f32):    2.25 MiB, S (f32):   48.00 MiB
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 1
sched_reserve: reserving ...
sched_reserve: max_nodes = 20480
sched_reserve: reserving full memory module
sched_reserve: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 1
sched_reserve: resolving fused Gated Delta Net support:
graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
sched_reserve: fused Gated Delta Net (autoregressive) enabled
graph_reserve: reserving a graph for ubatch with n_tokens =   16, n_seqs =  1, n_outputs =   16
sched_reserve: fused Gated Delta Net (chunked) enabled
graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
graph_reserve: reserving a graph for ubatch with n_tokens =    1, n_seqs =  1, n_outputs =    1
graph_reserve: reserving a graph for ubatch with n_tokens =  512, n_seqs =  1, n_outputs =  512
sched_reserve:        CPU compute buffer size =   495.00 MiB
sched_reserve: graph nodes  = 1833
sched_reserve: graph splits = 1
sched_reserve: reserve took 5.17 ms, sched copies = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
set_warmup: value = 1                                                                                                                                                                                                                      \set_warmup: value = 0
srv    load_model: initializing slots, n_slots = 1                                                                                                                                                                                         |common_speculative_is_compat: the target context does not support partial sequence removal                                                                                                                                                 /srv    load_model: speculative decoding not supported by this context
slot   load_model: id  0 | task -1 | new slot, n_ctx = 4096
slot        reset: id  0 | task -1 |
srv    load_model: prompt cache is enabled, size limit: 8192 MiB
srv    load_model: use `--cache-ram 0` to disable the prompt cache
srv    load_model: for more info see https://github.com/ggml-org/llama.cpp/pull/16391
common_chat_templates_apply_jinja: using differential autoparser
=== Starting differential analysis ===
Phase 1: Reasoning analysis
Phase 2: Content analysis
Phase 3: Tool call analysis

--- Reasoning & Content Structure ---
reasoning_mode: TAG_BASED
reasoning_start: '<think>'
reasoning_end: '</think>'
content_mode: PLAIN
content_start: ''
content_end: ''

--- Tool Call Structure ---
tool_mode: JSON_NATIVE
supports_tools: true
supports_parallel_calls: true
tool_section_start: '<tool_call>
'
tool_section_end: '</tool_call>'
per_call_start: ''
per_call_end: ''
func_name_prefix: ''
func_name_suffix: ''
func_close: ''
arg_name_prefix: ''
arg_name_suffix: ''
arg_value_prefix: ''
arg_value_suffix: ''
name_field: 'name'
args_field: 'arguments'
id_field: ''
gen_id_field: ''
parameter_order: 'name, arguments'
=== Differential analysis complete ===
common_chat_templates_apply_jinja: generated parser:
Sequence(Literal(<|im_start|>assistant
), Epsilon, Tag(content, Until()), End)

parser generation prompt: <|im_start|>assistant
<think>

init: chat template, example_format: '<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Hello<|im_end|>
<|im_start|>assistant
Hi there<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
<think>
'
common_chat_templates_apply_jinja: using differential autoparser
=== Starting differential analysis ===
Phase 1: Reasoning analysis
Phase 2: Content analysis
Phase 3: Tool call analysis

--- Reasoning & Content Structure ---
reasoning_mode: TAG_BASED
reasoning_start: '<think>'
reasoning_end: '</think>'
content_mode: PLAIN
content_start: ''
content_end: ''

--- Tool Call Structure ---
tool_mode: JSON_NATIVE
supports_tools: true
supports_parallel_calls: true
tool_section_start: '<tool_call>
'
tool_section_end: '</tool_call>'                                                                                                                                                                                                           per_call_start: ''
per_call_end: ''
func_name_prefix: ''
func_name_suffix: ''
func_close: ''
arg_name_prefix: ''
 arg_name_suffix: ''
arg_value_prefix: ''
arg_value_suffix: ''
name_field: 'name'

args_field: 'arguments'
id_field: ''
gen_id_field: ''
parameter_order: 'name, arguments'
=== Differential analysis complete ===
common_chat_templates_apply_jinja: generated parser:
Sequence(Literal(<|im_start|>assistant
), Repetition(Sequence(Literal(<think>), Tag(reasoning, Until(</think>)), Literal(</think>), Space), 0, 1), Tag(content, Until()), End)

parser generation prompt: <|im_start|>assistant
<think>

srv          init: init: chat template, thinking = 1


▄▄ ▄▄
██ ██
██ ██  ▀▀█▄ ███▄███▄  ▀▀█▄    ▄████ ████▄ ████▄
██ ██ ▄█▀██ ██ ██ ██ ▄█▀██    ██    ██ ██ ██ ██
██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀
                                    ██    ██
                                    ▀▀    ▀▀

build      : b8533-0fac87b15
model      : Qwen3.5-4B-Abliterated-Claude-4.6-Opus-Reasoning-Distilled.i1-Q6_K.gguf
modalities : text

available commands:
  /exit or Ctrl+C     stop or exit
  /regen              regenerate the last response
  /clear              clear the chat history
  /read               add a text file

que    start_loop: processing new tasks
que    start_loop: update slots
srv  update_slots: all slots are idle
que    start_loop: waiting for new tasks

> Ol�! Me diga quem voc� � em uma frase.

common_chat_templates_apply_jinja: using differential autoparser
=== Starting differential analysis ===
Phase 1: Reasoning analysis
Phase 2: Content analysis
Phase 3: Tool call analysis

--- Reasoning & Content Structure ---
reasoning_mode: TAG_BASED
reasoning_start: '<think>'
reasoning_end: '</think>'
content_mode: PLAIN
content_start: ''
content_end: ''

--- Tool Call Structure ---
tool_mode: JSON_NATIVE
supports_tools: true
supports_parallel_calls: true
tool_section_start: '<tool_call>
'
tool_section_end: '</tool_call>'
per_call_start: ''
per_call_end: ''
func_name_prefix: ''
func_name_suffix: ''
func_close: ''
arg_name_prefix: ''
arg_name_suffix: ''
arg_value_prefix: ''
arg_value_suffix: ''
name_field: 'name'
args_field: 'arguments'
id_field: ''
gen_id_field: ''
parameter_order: 'name, arguments'
=== Differential analysis complete ===
common_chat_templates_apply_jinja: generated parser:
Sequence(Literal(<|im_start|>assistant
), Repetition(Sequence(Literal(<think>), Tag(reasoning, Until(</think>)), Literal(</think>), Space), 0, 1), Tag(content, Until()), End)

parser generation prompt: <|im_start|>assistant
<think>

common_chat_templates_apply_jinja: using differential autoparser
=== Starting differential analysis ===
Phase 1: Reasoning analysis
Phase 2: Content analysis
Phase 3: Tool call analysis

--- Reasoning & Content Structure ---
reasoning_mode: TAG_BASED
reasoning_start: '<think>'
reasoning_end: '</think>'
content_mode: PLAIN
content_start: ''
content_end: ''

--- Tool Call Structure ---
tool_mode: JSON_NATIVE
supports_tools: true
supports_parallel_calls: true
tool_section_start: '<tool_call>
'
tool_section_end: '</tool_call>'
per_call_start: ''
per_call_end: ''
func_name_prefix: ''
func_name_suffix: ''
func_close: ''
arg_name_prefix: ''
arg_name_suffix: ''
arg_value_prefix: ''
arg_value_suffix: ''
name_field: 'name'
args_field: 'arguments'
id_field: ''
gen_id_field: ''
parameter_order: 'name, arguments'
=== Differential analysis complete ===
common_chat_templates_apply_jinja: generated parser:
Sequence(Literal(<|im_start|>assistant
), Repetition(Sequence(Literal(<think>), Tag(reasoning, Until(</think>)), Literal(</think>), Space), 0, 1), Tag(content, Until()), End)

parser generation prompt: <|im_start|>assistant
<think>

res  add_waiting_: add task 0 to waiting list. current waiting = 0 (before add)
que          post: new task, id = 0, front = 0
/srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 1
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 2, front = 0
slot update_slots: id  0 | task 0 | n_tokens = 19, memory_seq_rm [19, end)
slot init_sampler: id  0 | task 0 | init sampler, took 0.00 ms, tokens: text = 23, total = 23
slot update_slots: id  0 | task 0 | prompt processing done, n_tokens = 23, batch.n_tokens = 4
srv  update_slots: decoding batch, n_tokens = 4
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0                                                                                                                                                                                                                  -res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 1, n_remaining = 79, next token:  9764 'Let'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let                                                                                                                                                                                               que    start_loop: processing task, id = 2
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let
que          post: new task, id = 3, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 24, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
 adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0                                                                                                                                                                                                                  [Start thinking]

Letres          send: sending result for task id = 0
 meres          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 2, n_remaining = 78, next token:   728 ' me'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me
que    start_loop: processing task, id = 3
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me
que          post: new task, id = 4, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 25, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 3, n_remaining = 77, next token:  1683 ' think'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think
que    start_loop: processing task, id = 4
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think
que          post: new task, id = 5, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 26, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
 thinkadapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 4, n_remaining = 76, next token:  1472 ' through'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 5
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
srv  update_chat_: Parsing chat message: Let me think through
que          post: new task, id = 6, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 27, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
 throughres          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 5, n_remaining = 75, next token:   411 ' this'
 thissrv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 6
srv  update_chat_: Parsing chat message: Let me think through this
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 7, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 28, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 6, n_remaining = 74, next token: 15060 ' carefully'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
 carefullyque    start_loop: processing task, id = 7
srv  update_chat_: Parsing chat message: Let me think through this carefully
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 8, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 29, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 7, n_remaining = 73, next token:    13 '.'
srv  update_slots: run slots completed
.que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 8
srv  update_chat_: Parsing chat message: Let me think through this carefully.
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 9, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 30, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 8, n_remaining = 72, next token:   271 '

'


srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.


que    start_loop: processing task, id = 9
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.


que          post: new task, id = 10, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 31, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 9, n_remaining = 71, next token:   760 'The'
Thesrv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 10
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 11, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 32, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
 questionres          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 10, n_remaining = 70, next token:  3296 ' question'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 11
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 12, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 33, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 11, n_remaining = 69, next token: 56231 '/task'
/tasksrv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task
que    start_loop: processing task, id = 12
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 13, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 34, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 12, n_remaining = 68, next token:    25 ':'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 13
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task:
:que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 14, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task:
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 35, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 13, n_remaining = 67, next token: 11620 ' Ol'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
 Olque    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol
que    start_loop: processing task, id = 14
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 15, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 36, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 14, n_remaining = 66, next token:  5513 '�'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�
que    start_loop: processing task, id = 15
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�
que          post: new task, id = 16, front = 0
�slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 37, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 15, n_remaining = 65, next token:     0 '!'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
!que    start_loop: processing new tasks
que    start_loop: processing task, id = 16
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�!
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 17, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�!
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 38, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
 Meres          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 16, n_remaining = 64, next token:  2088 ' Me'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 17
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 18, front = 0
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 39, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 17, n_remaining = 63, next token: 210906 ' diga'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 18
que    start_loop: update slots
 digasrv  update_slots: posting NEXT_RESPONSE
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga
que          post: new task, id = 19, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 40, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 18, n_remaining = 62, next token: 55675 ' quem'
 quemsrv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem
que    start_loop: processing task, id = 19
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem
que          post: new task, id = 20, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 41, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 19, n_remaining = 61, next token: 11643 ' voc'
 vocsrv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 20
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc
que          post: new task, id = 21, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 42, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
�slot process_toke: id  0 | task 0 | n_decoded = 20, n_remaining = 60, next token:  5513 '�'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 21
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 22, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 43, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc�
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc�
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 21, n_remaining = 59, next token: 28373 ' �'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
 �que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� �
que    start_loop: processing task, id = 22
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� �
que          post: new task, id = 23, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 44, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 22, n_remaining = 58, next token:   945 ' em'
srv  update_slots: run slots completed
 emque    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em
que    start_loop: processing task, id = 23
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em
que          post: new task, id = 24, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 45, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 23, n_remaining = 57, next token: 10293 ' uma'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
 umasrv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma
que    start_loop: processing task, id = 24
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma
que          post: new task, id = 25, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 46, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 24, n_remaining = 56, next token: 87222 ' frase'
srv  update_slots: run slots completed
 fraseque    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase
que    start_loop: processing task, id = 25
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase
que          post: new task, id = 26, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 47, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 25, n_remaining = 55, next token:    13 '.'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.
que    start_loop: processing task, id = 26
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.
que          post: new task, id = 27, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 48, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
.set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 26, n_remaining = 54, next token:   271 '

'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks


que    start_loop: processing new tasks
que    start_loop: processing task, id = 27
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.


que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 28, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.


slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 49, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 27, n_remaining = 53, next token:  4888 'My'
srv  update_slots: run slots completed
Myque    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My
que    start_loop: processing task, id = 28
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My
que          post: new task, id = 29, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 50, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 28, n_remaining = 52, next token: 31626 ' reasoning'
srv  update_slots: run slots completed
 reasoningque    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 29
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 30, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 51, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 29, n_remaining = 51, next token:    25 ':'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning:
:que    start_loop: processing task, id = 30
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning:
que          post: new task, id = 31, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 52, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 30, n_remaining = 50, next token: 179033 ' Quem'
srv  update_slots: run slots completed
 Quemque    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 31
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 32, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 53, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 31, n_remaining = 49, next token:  5641 ' sou'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
 souque    start_loop: processing new tasks
que    start_loop: processing task, id = 32
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou
que          post: new task, id = 33, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 54, truncated = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 32, n_remaining = 48, next token: 15098 ' eu'
 eusrv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu
que    start_loop: processing task, id = 33
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu
que          post: new task, id = 34, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 55, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
 emres          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 33, n_remaining = 47, next token:   945 ' em'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 34
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 35, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 56, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 34, n_remaining = 46, next token: 10293 ' uma'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma
que    start_loop: processing task, id = 35
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
 umaParsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma
que          post: new task, id = 36, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 57, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 35, n_remaining = 45, next token: 87222 ' frase'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
 fraseque    start_loop: processing new tasks
que    start_loop: processing task, id = 36
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 37, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 58, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 36, n_remaining = 44, next token:    30 '?'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
que    start_loop: processing task, id = 37
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
que          post: new task, id = 38, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 59, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
?set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 37, n_remaining = 43, next token:   198 '
'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 38

srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?

que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 39, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?

slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 60, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 38, n_remaining = 42, next token: 248069 '</think>'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 39
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 40, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 61, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 39, n_remaining = 41, next token:   271 '

'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>


que    start_loop: processing task, id = 40
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 41, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>


slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 62, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 40, n_remaining = 40, next token:   332 '**'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 41

[End thinking]

srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 42, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 63, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
**res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 41, n_remaining = 39, next token:  2114 'Qu'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
Quque    start_loop: processing task, id = 42
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Qu
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 43, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Qu
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 64, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 42, n_remaining = 38, next token:   334 'em'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem
que    start_loop: processing task, id = 43
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem
que          post: new task, id = 44, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 65, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
emres          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 43, n_remaining = 37, next token:  5641 ' sou'
 sousrv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou
que    start_loop: processing task, id = 44
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 45, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 66, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 44, n_remaining = 36, next token: 15098 ' eu'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 45
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 46, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 67, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
 euset_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 45, n_remaining = 35, next token:   945 ' em'
srv  update_slots: run slots completed
 emque    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em
que    start_loop: processing task, id = 46
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em
que          post: new task, id = 47, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 68, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 46, n_remaining = 34, next token: 10293 ' uma'
 umasrv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 47
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma
que          post: new task, id = 48, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 69, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 47, n_remaining = 33, next token: 87222 ' frase'
 frasesrv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 48
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase
que          post: new task, id = 49, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 70, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 48, n_remaining = 32, next token: 170286 '?**'
?**srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 49
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 50, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 71, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 49, n_remaining = 31, next token:   271 '

'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 50


srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**


que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 51, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**


slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 72, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 50, n_remaining = 30, next token: 55509 'Sou'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 51
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 52, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou
Souslot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 73, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 51, n_remaining = 29, next token:  4297 ' um'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
 umque    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um
que    start_loop: processing task, id = 52
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um
que          post: new task, id = 53, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 74, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 52, n_remaining = 28, next token:  2972 ' **'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 53
 **que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **
que          post: new task, id = 54, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 75, truncated = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 53, n_remaining = 27, next token: 49250 'assist'
srv  update_slots: run slots completed
assistque    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assist
que    start_loop: processing task, id = 54
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assist
que          post: new task, id = 55, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 76, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 54, n_remaining = 26, next token:  6603 'ente'
entesrv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 55
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 56, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 77, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 55, n_remaining = 25, next token:   401 ' de'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 56
que    start_loop: update slots
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 57, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 78, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
 deres          send: sending result for task id = 0
 inteligênciares          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 56, n_remaining = 24, next token: 235635 ' inteligência'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência
que    start_loop: processing task, id = 57
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência
que          post: new task, id = 58, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 79, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 57, n_remaining = 23, next token: 19820 ' artificial'
srv  update_slots: run slots completed
 artificialque    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 58
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial
que          post: new task, id = 59, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 80, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
**,res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 58, n_remaining = 22, next token: 93868 '**,'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 59
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**,
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 60, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**,
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 81, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 59, n_remaining = 21, next token: 212482 ' desenvolvido'
srv  update_slots: run slots completed
 desenvolvidoque    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 60
que    start_loop: update slots
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido
que          post: new task, id = 61, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 82, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 60, n_remaining = 20, next token:  4018 ' por'
 porsrv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por
que    start_loop: processing task, id = 61
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 62, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 83, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 61, n_remaining = 19, next token: 10293 ' uma'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 62
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
 umaque          post: new task, id = 63, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 84, truncated = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 62, n_remaining = 18, next token: 23828 ' grande'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 63
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
 grandeParsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande
que          post: new task, id = 64, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 85, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 63, n_remaining = 17, next token: 31678 ' empresa'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa
que    start_loop: processing task, id = 64
que    start_loop: update slots
 empresasrv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 65, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 86, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 64, n_remaining = 16, next token:   401 ' de'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 65
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 66, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de
 deslot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 87, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 65, n_remaining = 15, next token: 173238 ' tecnologia'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia
que    start_loop: processing task, id = 66
que    start_loop: update slots
 tecnologiasrv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia
que          post: new task, id = 67, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 88, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 66, n_remaining = 14, next token:   318 ' ('
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 67
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (
 (que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 68, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 89, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 67, n_remaining = 13, next token: 41528 'prov'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 68
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (prov
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (prov
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 69, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 90, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
provadapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 68, n_remaining = 12, next token: 164861 'avelmente'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 69
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente
que          post: new task, id = 70, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente
avelmenteslot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 91, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 69, n_remaining = 11, next token:   264 ' a'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
 aque    start_loop: processing task, id = 70
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a
que          post: new task, id = 71, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 92, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 70, n_remaining = 10, next token:  4923 ' Google'
srv  update_slots: run slots completed
 Googleque    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google
que    start_loop: processing task, id = 71
que    start_loop: update slots
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 72, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 93, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 71, n_remaining = 9, next token:    11 ','
,srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 72
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google,
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 73, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google,
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 94, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 72, n_remaining = 8, next token: 55854 ' dado'
srv  update_slots: run slots completed
 dadoque    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado
que    start_loop: processing task, id = 73
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado
que          post: new task, id = 74, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 95, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 73, n_remaining = 7, next token:   296 ' o'
srv  update_slots: run slots completed
 oque    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 74
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 75, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 96, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 74, n_remaining = 6, next token: 74118 ' contexto'
 contextosrv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 75
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto
que          post: new task, id = 76, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 97, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 75, n_remaining = 5, next token: 180024 ' comum'
srv  update_slots: run slots completed
 comumque    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum
que    start_loop: processing task, id = 76
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 77, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 98, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 76, n_remaining = 4, next token:   945 ' em'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
 emque    start_loop: processing task, id = 77
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum em
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 78, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum em
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 99, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 77, n_remaining = 3, next token: 32208 ' Portugal'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
 Portugalque    start_loop: processing task, id = 78
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum em Portugal
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 79, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum em Portugal
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 100, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 78, n_remaining = 2, next token:   553 ').'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum em Portugal).
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum em Portugal).
que    start_loop: processing task, id = 79
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 80, front = 0
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 101, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
).set_adapters_lora: adapters = 0000000000000000
adapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | n_decoded = 79, n_remaining = 1, next token: 18306 ' Pos'
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum em Portugal). Pos
que    start_loop: processing task, id = 80
que    start_loop: update slots
srv  update_slots: posting NEXT_RESPONSE
que          post: new task, id = 81, front = 0
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum em Portugal). Pos
slot update_slots: id  0 | task 0 | slot decode token, n_ctx = 4096, n_tokens = 102, truncated = 0
srv  update_slots: decoding batch, n_tokens = 1
set_adapters_lora: adapters = 0000000000000000
 Posadapters_lora_are_same: adapters = 0000000000000000
set_embeddings: value = 0
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot process_toke: id  0 | task 0 | stopped by limit, n_decoded = 80, n_predict = 80
soslot process_toke: id  0 | task 0 | n_decoded = 80, n_remaining = 0, next token:   684 'so'
slot print_timing: id  0 | task 0 |
prompt eval time =     579.36 ms /    23 tokens (   25.19 ms per token,    39.70 tokens per second)
       eval time =    9646.39 ms /    80 tokens (  120.58 ms per token,     8.29 tokens per second)
      total time =   10225.75 ms /   103 tokens

srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum em Portugal). Posso
res          send: sending result for task id = 0
res          send: task id = 0 pushed to result queue
slot      release: id  0 | task 0 | stop processing: n_tokens = 102, truncated = 0
slot        reset: id  0 | task 0 |
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum em Portugal). Posso
srv  update_slots: run slots completed
que    start_loop: waiting for new tasks
que    start_loop: processing new tasks
que    start_loop: processing task, id = 81
que    start_loop: update slots
srv  update_slots: all slots are idle
que    start_loop: waiting for new tasks
srv  update_chat_: Parsing chat message: Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum em Portugal). Posso
Parsing PEG input with format peg-native: <|im_start|>assistant
<think>
Let me think through this carefully.

The question/task: Ol�! Me diga quem voc� � em uma frase.

My reasoning: Quem sou eu em uma frase?
</think>

**Quem sou eu em uma frase?**

Sou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum em Portugal). Posso
Parsed message: {"role":"assistant","content":"**Quem sou eu em uma frase?**\n\nSou um **assistente de inteligência artificial**, desenvolvido por uma grande empresa de tecnologia (provavelmente a Google, dado o contexto comum em Portugal). Posso","reasoning_content":"\nLet me think through this carefully.\n\nThe question/task: Ol�! Me diga quem voc� � em uma frase.\n\nMy reasoning: Quem sou eu em uma frase?\n"}
res  remove_waiti: remove task 0 from waiting list. current waiting = 1 (before remove)
srv          stop: all tasks already finished, no need to cancel

[ Prompt: 39.7 t/s | Generation: 8.3 t/s ]

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