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 '' 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 '' 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 '' 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 '' 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 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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 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 '' 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 '' 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 '' 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 '' 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 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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: '' reasoning_end: '' 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_section_end: '' 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 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 ' 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: '' reasoning_end: '' 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_section_end: '' 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(), Tag(reasoning, Until()), Literal(), Space), 0, 1), Tag(content, Until()), End) parser generation prompt: <|im_start|>assistant 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: '' reasoning_end: '' 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_section_end: '' 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(), Tag(reasoning, Until()), Literal(), Space), 0, 1), Tag(content, Until()), End) parser generation prompt: <|im_start|>assistant 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: '' reasoning_end: '' 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_section_end: '' 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(), Tag(reasoning, Until()), Literal(), Space), 0, 1), Tag(content, Until()), End) parser generation prompt: <|im_start|>assistant 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 '' 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? 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 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 = 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? 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 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 = 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? ** 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 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 = 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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **Quem sou eu em uma frase?** Sou um **assistente de inteligência artificial Parsing PEG input with format peg-native: <|im_start|>assistant 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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? **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 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? **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 ] >