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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
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
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create_tensor: loading tensor blk.30.attn_norm.weight
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create_tensor: loading tensor blk.31.attn_q_norm.weight
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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
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create_tensor: loading tensor blk.11.attn_output.weight
create_tensor: loading tensor blk.11.attn_q_norm.weight
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create_tensor: loading tensor blk.11.ffn_gate.weight
create_tensor: loading tensor blk.11.ffn_down.weight
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create_tensor: loading tensor blk.12.attn_norm.weight
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create_tensor: loading tensor blk.12.ssm_conv1d.weight
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create_tensor: loading tensor blk.13.attn_norm.weight
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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
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create_tensor: loading tensor blk.13.ssm_alpha.weight
create_tensor: loading tensor blk.13.ssm_norm.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_dt.bias
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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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