From 40d49627fc455aa465d4150dcb2ab203d54e38fa Mon Sep 17 00:00:00 2001 From: Leon Date: Mon, 27 Jul 2026 17:00:05 +0800 Subject: [PATCH] 20260727-temp02 --- .gitignore | 7 + README.md | 29 + log | 1831 ++++++++++++++++++++++ uv.lock | 4274 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 6141 insertions(+) create mode 100644 log create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore index ff773c6684..7dc546298e 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,11 @@ .python-version +# UV-managed project environment and cache +.venv/ +uv-cache/ +.uv-cache/ + .vscode/ _skbuild/ @@ -10,6 +15,8 @@ _skbuild/ .direnv models/ +*.gguf +*.ggml # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/README.md b/README.md index c02df9eed0..f067b65aa9 100644 --- a/README.md +++ b/README.md @@ -696,6 +696,35 @@ pip install 'llama-cpp-python[server]' python3 -m llama_cpp.server --model models/7B/llama-model.gguf ``` +If you manage this repository with [UV](https://docs.astral.sh/uv/), install the server extra and run the server inside the UV-managed environment: + +```bash +uv sync --extra server +uv run python -m llama_cpp.server --model ./models/llama-model.gguf --host 0.0.0.0 --port 8000 +``` + +For local-only access, you can omit `--host 0.0.0.0` and use the default `localhost` binding: + +```bash +uv run python -m llama_cpp.server --model ./models/llama-model.gguf --port 8000 +uv run python -m llama_cpp.server --model ./models/gemma-4-12B-it-Q4_K_M.gguf --host 0.0.0.0 --port 8000 +``` + +If your local build supports GPU acceleration, offload layers to the GPU, for example: + +```bash +uv run python -m llama_cpp.server --model ./models/llama-model.gguf --host 0.0.0.0 --port 8000 --n_gpu_layers -1 +``` + +You can verify the running OpenAI-compatible API with: + +```bash +curl http://localhost:8000/v1/models +curl http://localhost:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{"model":"llama-model","messages":[{"role":"user","content":"Hello"}],"max_tokens":128}' +``` + Similar to Hardware Acceleration section above, you can also install with GPU (cuBLAS) support like this: ```bash diff --git a/log b/log new file mode 100644 index 0000000000..f19f147ea4 --- /dev/null +++ b/log @@ -0,0 +1,1831 @@ +AzureAD+LongYYYu@IBM-PF4F6Q3G MINGW64 /c/work/dev/llama-cpp-python (feature/win) +$ uv run python -m llama_cpp.server --model ./models/gemma-4-12B-it-Q4_K_M.gguf --port 8000 +llama_model_loader: loaded meta data with 45 key-value pairs and 667 tensors from ./models/gemma-4-12B-it-Q4_K_M.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 = gemma4 +llama_model_loader: - kv 1: general.type str = model +llama_model_loader: - kv 2: general.sampling.top_k i32 = 64 +llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000 +llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000 +llama_model_loader: - kv 5: general.name str = Google_Gemma 4 12B It +llama_model_loader: - kv 6: general.finetune str = it +llama_model_loader: - kv 7: general.basename str = google_gemma-4 +llama_model_loader: - kv 8: general.size_label str = 12B +llama_model_loader: - kv 9: gemma4.block_count u32 = 48 +llama_model_loader: - kv 10: gemma4.context_length u32 = 262144 +llama_model_loader: - kv 11: gemma4.embedding_length u32 = 3840 +llama_model_loader: - kv 12: gemma4.feed_forward_length u32 = 15360 +llama_model_loader: - kv 13: gemma4.attention.head_count u32 = 16 +llama_model_loader: - kv 14: gemma4.attention.head_count_kv arr[i32,48] = [8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 1, ... +llama_model_loader: - kv 15: gemma4.rope.freq_base f32 = 1000000.000000 +llama_model_loader: - kv 16: gemma4.rope.freq_base_swa f32 = 10000.000000 +llama_model_loader: - kv 17: gemma4.attention.layer_norm_rms_epsilon f32 = 0.000001 +llama_model_loader: - kv 18: gemma4.attention.key_length u32 = 512 +llama_model_loader: - kv 19: gemma4.attention.value_length u32 = 512 +llama_model_loader: - kv 20: gemma4.final_logit_softcapping f32 = 30.000000 +llama_model_loader: - kv 21: gemma4.attention.sliding_window u32 = 1024 +llama_model_loader: - kv 22: gemma4.attention.shared_kv_layers u32 = 0 +llama_model_loader: - kv 23: gemma4.embedding_length_per_layer_input u32 = 0 +llama_model_loader: - kv 24: gemma4.attention.sliding_window_pattern arr[bool,48] = [true, true, true, true, true, false,... +llama_model_loader: - kv 25: gemma4.attention.key_length_swa u32 = 256 +llama_model_loader: - kv 26: gemma4.attention.value_length_swa u32 = 256 +llama_model_loader: - kv 27: gemma4.rope.dimension_count u32 = 512 +llama_model_loader: - kv 28: gemma4.rope.dimension_count_swa u32 = 256 +llama_model_loader: - kv 29: tokenizer.ggml.suppress_tokens arr[i32,2] = [258883, 258882] +llama_model_loader: - kv 30: tokenizer.ggml.model str = gemma4 +llama_model_loader: - kv 31: tokenizer.ggml.tokens arr[str,262144] = ["", "", "", "", ... +llama_model_loader: - kv 32: tokenizer.ggml.scores arr[f32,262144] = [-1000.000000, -1000.000000, -1000.00... +llama_model_loader: - kv 33: tokenizer.ggml.token_type arr[i32,262144] = [3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, ... +llama_model_loader: - kv 34: tokenizer.ggml.merges arr[str,514906] = ["\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n", ... +llama_model_loader: - kv 35: tokenizer.ggml.bos_token_id u32 = 2 +llama_model_loader: - kv 36: tokenizer.ggml.eos_token_id u32 = 1 +llama_model_loader: - kv 37: tokenizer.ggml.unknown_token_id u32 = 3 +llama_model_loader: - kv 38: tokenizer.ggml.padding_token_id u32 = 0 +llama_model_loader: - kv 39: tokenizer.ggml.mask_token_id u32 = 4 +llama_model_loader: - kv 40: tokenizer.chat_template str = {#\n Template: Google Gemma 4 Canonic... +llama_model_loader: - kv 41: tokenizer.ggml.add_space_prefix bool = false +llama_model_loader: - kv 42: tokenizer.ggml.add_bos_token bool = true +llama_model_loader: - kv 43: general.quantization_version u32 = 2 +llama_model_loader: - kv 44: general.file_type u32 = 15 +llama_model_loader: - type f32: 338 tensors +llama_model_loader: - type q4_K: 284 tensors +llama_model_loader: - type q6_K: 45 tensors +print_info: file format = GGUF V3 (latest) +print_info: file type = Q4_K - Medium +print_info: file size = 6.86 GiB (4.95 BPW) +init_tokenizer: initializing tokenizer for type 2 +load: 0 unused tokens +load: control token: 0 '' is not marked as EOG +load: control token: 3 '' is not marked as EOG +load: control token: 2 '' is not marked as EOG +load: control token: 4 '' is not marked as EOG +load: control token: 47 '' is not marked as EOG +load: control token: 98 '<|think|>' is not marked as EOG +load: control token: 46 '<|tool>' is not marked as EOG +load: control-looking token: 50 '<|tool_response>' was not control-type; this is probably a bug in the model. its type will be overridden +load: control token: 105 '<|turn>' is not marked as EOG +load: control-looking token: 212 '' was not control-type; this is probably a bug in the model. its type will be overridden +load: control token: 258880 '<|image|>' is not marked as EOG +load: control token: 258882 '' is not marked as EOG +load: control token: 256000 '<|audio>' is not marked as EOG +load: control token: 258883 '' is not marked as EOG +load: control token: 258884 '<|video|>' is not marked as EOG +load: control token: 255999 '<|image>' is not marked as EOG +load: control token: 258881 '<|audio|>' is not marked as EOG +load: printing all EOG tokens: +load: - 1 ('') +load: - 50 ('<|tool_response>') +load: - 106 ('') +load: - 212 ('') +load: special_eog_ids contains '<|tool_response>', removing '' token from EOG list +load: special tokens cache size = 24 +load: token to piece cache size = 1.9445 MB +print_info: arch = gemma4 +print_info: vocab_only = 0 +print_info: no_alloc = 0 +print_info: n_ctx_train = 262144 +print_info: n_embd_inp = 3840 +print_info: n_embd = 3840 +print_info: n_embd_out = 3840 +print_info: n_layer = 48 +print_info: n_layer_all = 48 +print_info: n_head = 16 +print_info: n_head_kv = [8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 1, 8, 8, 8, 8, 8, 1] +print_info: n_rot = 512 +print_info: n_swa = 1024 +print_info: is_swa_any = 1 +print_info: n_embd_head_k = 512 +print_info: n_embd_head_v = 512 +print_info: n_gqa = [2, 2, 2, 2, 2, 16, 2, 2, 2, 2, 2, 16, 2, 2, 2, 2, 2, 16, 2, 2, 2, 2, 2, 16, 2, 2, 2, 2, 2, 16, 2, 2, 2, 2, 2, 16, 2, 2, 2, 2, 2, 16, 2, 2, 2, 2, 2, 16] +print_info: n_embd_k_gqa = [2048, 2048, 2048, 2048, 2048, 512, 2048, 2048, 2048, 2048, 2048, 512, 2048, 2048, 2048, 2048, 2048, 512, 2048, 2048, 2048, 2048, 2048, 512, 2048, 2048, 2048, 2048, 2048, 512, 2048, 2048, 2048, 2048, 2048, 512, 2048, 2048, 2048, 2048, 2048, 512, 2048, 2048, 2048, 2048, 2048, 512] +print_info: n_embd_v_gqa = [2048, 2048, 2048, 2048, 2048, 512, 2048, 2048, 2048, 2048, 2048, 512, 2048, 2048, 2048, 2048, 2048, 512, 2048, 2048, 2048, 2048, 2048, 512, 2048, 2048, 2048, 2048, 2048, 512, 2048, 2048, 2048, 2048, 2048, 512, 2048, 2048, 2048, 2048, 2048, 512, 2048, 2048, 2048, 2048, 2048, 512] +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 = 1.0e+00 +print_info: f_attn_value_scale = 0.0000 +print_info: n_ff = 15360 +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 = 2 +print_info: rope scaling = linear +print_info: freq_base_train = 1000000.0 +print_info: freq_scale_train = 1 +print_info: freq_base_swa = 10000.0 +print_info: freq_scale_swa = 1 +print_info: n_embd_head_k_swa = 256 +print_info: n_embd_head_v_swa = 256 +print_info: n_rot_swa = 256 +print_info: n_ctx_orig_yarn = 262144 +print_info: rope_yarn_log_mul = 0.0000 +print_info: rope_finetuned = unknown +print_info: model type = ?B +print_info: model params = 11.91 B +print_info: general.name = Google_Gemma 4 12B It +print_info: vocab type = BPE +print_info: n_vocab = 262144 +print_info: n_merges = 514906 +print_info: BOS token = 2 '' +print_info: EOS token = 1 '' +print_info: UNK token = 3 '' +print_info: PAD token = 0 '' +print_info: MASK token = 4 '' +print_info: LF token = 107 ' +' +print_info: EOG token = 1 '' +print_info: EOG token = 50 '<|tool_response>' +print_info: EOG token = 106 '' +print_info: max token length = 93 +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 = 1 +load_tensors: layer 1 assigned to device CPU, is_swa = 1 +load_tensors: layer 2 assigned to device CPU, is_swa = 1 +load_tensors: layer 3 assigned to device CPU, is_swa = 1 +load_tensors: layer 4 assigned to device CPU, is_swa = 1 +load_tensors: layer 5 assigned to device CPU, is_swa = 0 +load_tensors: layer 6 assigned to device CPU, is_swa = 1 +load_tensors: layer 7 assigned to device CPU, is_swa = 1 +load_tensors: layer 8 assigned to device CPU, is_swa = 1 +load_tensors: layer 9 assigned to device CPU, is_swa = 1 +load_tensors: layer 10 assigned to device CPU, is_swa = 1 +load_tensors: layer 11 assigned to device CPU, is_swa = 0 +load_tensors: layer 12 assigned to device CPU, is_swa = 1 +load_tensors: layer 13 assigned to device CPU, is_swa = 1 +load_tensors: layer 14 assigned to device CPU, is_swa = 1 +load_tensors: layer 15 assigned to device CPU, is_swa = 1 +load_tensors: layer 16 assigned to device CPU, is_swa = 1 +load_tensors: layer 17 assigned to device CPU, is_swa = 0 +load_tensors: layer 18 assigned to device CPU, is_swa = 1 +load_tensors: layer 19 assigned to device CPU, is_swa = 1 +load_tensors: layer 20 assigned to device CPU, is_swa = 1 +load_tensors: layer 21 assigned to device CPU, is_swa = 1 +load_tensors: layer 22 assigned to device CPU, is_swa = 1 +load_tensors: layer 23 assigned to device CPU, is_swa = 0 +load_tensors: layer 24 assigned to device CPU, is_swa = 1 +load_tensors: layer 25 assigned to device CPU, is_swa = 1 +load_tensors: layer 26 assigned to device CPU, is_swa = 1 +load_tensors: layer 27 assigned to device CPU, is_swa = 1 +load_tensors: layer 28 assigned to device CPU, is_swa = 1 +load_tensors: layer 29 assigned to device CPU, is_swa = 0 +load_tensors: layer 30 assigned to device CPU, is_swa = 1 +load_tensors: layer 31 assigned to device CPU, is_swa = 1 +load_tensors: layer 32 assigned to device CPU, is_swa = 1 +load_tensors: layer 33 assigned to device CPU, is_swa = 1 +load_tensors: layer 34 assigned to device CPU, is_swa = 1 +load_tensors: layer 35 assigned to device CPU, is_swa = 0 +load_tensors: layer 36 assigned to device CPU, is_swa = 1 +load_tensors: layer 37 assigned to device CPU, is_swa = 1 +load_tensors: layer 38 assigned to device CPU, is_swa = 1 +load_tensors: layer 39 assigned to device CPU, is_swa = 1 +load_tensors: layer 40 assigned to device CPU, is_swa = 1 +load_tensors: layer 41 assigned to device CPU, is_swa = 0 +load_tensors: layer 42 assigned to device CPU, is_swa = 1 +load_tensors: layer 43 assigned to device CPU, is_swa = 1 +load_tensors: layer 44 assigned to device CPU, is_swa = 1 +load_tensors: layer 45 assigned to device CPU, is_swa = 1 +load_tensors: layer 46 assigned to device CPU, is_swa = 1 +load_tensors: layer 47 assigned to device CPU, is_swa = 0 +load_tensors: layer 48 assigned to device CPU, is_swa = 0 +create_tensor: loading tensor token_embd.weight +create_tensor: loading tensor 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q4_K_8x8 +repack: repack tensor blk.25.ffn_down.weight with q4_K_8x8 +.repack: repack tensor blk.26.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.26.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.26.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.26.ffn_gate.weight with q4_K_8x8 +repack: repack tensor blk.26.ffn_up.weight with q4_K_8x8 +.repack: repack tensor blk.27.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.27.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.27.attn_v.weight with q4_K_8x8 +repack: repack tensor blk.27.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.27.ffn_gate.weight with q4_K_8x8 +repack: repack tensor blk.27.ffn_up.weight with q4_K_8x8 +.repack: repack tensor blk.27.ffn_down.weight with q4_K_8x8 +repack: repack tensor blk.28.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.28.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.28.attn_v.weight with q4_K_8x8 +repack: repack tensor blk.28.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.28.ffn_gate.weight with q4_K_8x8 +.repack: repack tensor blk.28.ffn_up.weight with q4_K_8x8 +repack: repack tensor blk.28.ffn_down.weight with q4_K_8x8 +.repack: repack tensor blk.29.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.29.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.29.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.29.ffn_gate.weight with q4_K_8x8 +.repack: repack tensor blk.29.ffn_up.weight with q4_K_8x8 +repack: repack tensor blk.30.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.30.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.30.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.30.ffn_gate.weight with q4_K_8x8 +.repack: repack tensor blk.30.ffn_up.weight with q4_K_8x8 +repack: repack tensor blk.30.ffn_down.weight with q4_K_8x8 +repack: repack tensor blk.31.attn_q.weight with q4_K_8x8 +.repack: repack tensor blk.31.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.31.attn_v.weight with q4_K_8x8 +repack: repack tensor blk.31.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.31.ffn_gate.weight with q4_K_8x8 +repack: repack tensor blk.31.ffn_up.weight with q4_K_8x8 +.repack: repack tensor blk.31.ffn_down.weight with q4_K_8x8 +repack: repack tensor blk.32.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.32.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.32.attn_v.weight with q4_K_8x8 +repack: repack tensor blk.32.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.32.ffn_gate.weight with q4_K_8x8 +.repack: repack tensor blk.32.ffn_up.weight with q4_K_8x8 +repack: repack tensor blk.33.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.33.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.33.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.33.ffn_gate.weight with q4_K_8x8 +.repack: repack tensor blk.33.ffn_up.weight with q4_K_8x8 +repack: repack tensor blk.33.ffn_down.weight with q4_K_8x8 +.repack: repack tensor blk.34.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.34.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.34.attn_v.weight with q4_K_8x8 +repack: repack tensor blk.34.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.34.ffn_gate.weight with q4_K_8x8 +repack: repack tensor blk.34.ffn_up.weight with q4_K_8x8 +.repack: repack tensor blk.34.ffn_down.weight with q4_K_8x8 +repack: repack tensor blk.35.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.35.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.35.attn_output.weight with q4_K_8x8 +.repack: repack tensor blk.35.ffn_gate.weight with q4_K_8x8 +repack: repack tensor blk.35.ffn_up.weight with q4_K_8x8 +repack: repack tensor blk.36.attn_q.weight with q4_K_8x8 +.repack: repack tensor blk.36.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.36.attn_v.weight with q4_K_8x8 +repack: repack tensor blk.36.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.36.ffn_gate.weight with q4_K_8x8 +repack: repack tensor blk.36.ffn_up.weight with q4_K_8x8 +.repack: repack tensor blk.36.ffn_down.weight with q4_K_8x8 +repack: repack tensor blk.37.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.37.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.37.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.37.ffn_gate.weight with q4_K_8x8 +.repack: repack tensor blk.37.ffn_up.weight with q4_K_8x8 +repack: repack tensor blk.37.ffn_down.weight with q4_K_8x8 +repack: repack tensor blk.38.attn_q.weight with q4_K_8x8 +.repack: repack tensor blk.38.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.38.attn_v.weight with q4_K_8x8 +repack: repack tensor blk.38.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.38.ffn_gate.weight with q4_K_8x8 +repack: repack tensor blk.38.ffn_up.weight with q4_K_8x8 +.repack: repack tensor blk.39.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.39.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.39.attn_v.weight with q4_K_8x8 +repack: repack tensor blk.39.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.39.ffn_gate.weight with q4_K_8x8 +repack: repack tensor blk.39.ffn_up.weight with q4_K_8x8 +.repack: repack tensor blk.39.ffn_down.weight with q4_K_8x8 +repack: repack tensor blk.40.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.40.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.40.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.40.ffn_gate.weight with q4_K_8x8 +.repack: repack tensor blk.40.ffn_up.weight with q4_K_8x8 +repack: repack tensor blk.40.ffn_down.weight with q4_K_8x8 +.repack: repack tensor blk.41.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.41.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.41.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.41.ffn_gate.weight with q4_K_8x8 +repack: repack tensor blk.41.ffn_up.weight with q4_K_8x8 +.repack: repack tensor blk.42.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.42.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.42.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.42.ffn_gate.weight with q4_K_8x8 +.repack: repack tensor blk.42.ffn_up.weight with q4_K_8x8 +repack: repack tensor blk.43.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.43.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.43.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.43.ffn_gate.weight with q4_K_8x8 +.repack: repack tensor blk.43.ffn_up.weight with q4_K_8x8 +repack: repack tensor blk.44.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.44.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.44.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.44.ffn_gate.weight with q4_K_8x8 +.repack: repack tensor blk.44.ffn_up.weight with q4_K_8x8 +repack: repack tensor blk.45.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.45.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.45.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.45.ffn_gate.weight with q4_K_8x8 +.repack: repack tensor blk.45.ffn_up.weight with q4_K_8x8 +repack: repack tensor blk.46.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.46.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.46.attn_output.weight with q4_K_8x8 +repack: repack tensor blk.46.ffn_gate.weight with q4_K_8x8 +.repack: repack tensor blk.46.ffn_up.weight with q4_K_8x8 +repack: repack tensor blk.47.attn_q.weight with q4_K_8x8 +repack: repack tensor blk.47.attn_k.weight with q4_K_8x8 +repack: repack tensor blk.47.attn_output.weight with q4_K_8x8 +.repack: repack tensor blk.47.ffn_gate.weight with q4_K_8x8 +repack: repack tensor blk.47.ffn_up.weight with q4_K_8x8 +. +llama_context: constructing llama_context +llama_context: n_seq_max = 1 +llama_context: n_ctx = 2048 +llama_context: n_ctx_seq = 2048 +llama_context: n_batch = 512 +llama_context: n_ubatch = 512 +llama_context: causal_attn = 1 +llama_context: flash_attn = disabled +llama_context: kv_unified = false +llama_context: freq_base = 1000000.0 +llama_context: freq_scale = 1 +llama_context: n_rs_seq = 0 +llama_context: n_outputs_max = 512 +llama_context: n_ctx_seq (2048) < n_ctx_train (262144) -- the full capacity of the model will not be utilized +set_abort_callback: call +llama_context: CPU output buffer size = 1.00 MiB +llama_kv_cache_iswa: using full-size SWA cache (ref: https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055) +llama_kv_cache_iswa: creating non-SWA KV cache, size = 2048 cells +llama_kv_cache: the V embeddings have different sizes across layers and FA is not enabled - padding V cache to 2048 +llama_kv_cache: layer 0: filtered +llama_kv_cache: layer 1: filtered +llama_kv_cache: layer 2: filtered +llama_kv_cache: layer 3: filtered +llama_kv_cache: layer 4: filtered +llama_kv_cache: layer 5: dev = CPU +llama_kv_cache: layer 6: filtered +llama_kv_cache: layer 7: filtered +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: filtered +llama_kv_cache: layer 16: filtered +llama_kv_cache: layer 17: dev = CPU +llama_kv_cache: layer 18: filtered +llama_kv_cache: layer 19: filtered +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: filtered +llama_kv_cache: layer 28: filtered +llama_kv_cache: layer 29: dev = CPU +llama_kv_cache: layer 30: filtered +llama_kv_cache: layer 31: filtered +llama_kv_cache: layer 32: filtered +llama_kv_cache: layer 33: filtered +llama_kv_cache: layer 34: filtered +llama_kv_cache: layer 35: dev = CPU +llama_kv_cache: layer 36: filtered +llama_kv_cache: layer 37: filtered +llama_kv_cache: layer 38: filtered +llama_kv_cache: layer 39: filtered +llama_kv_cache: layer 40: filtered +llama_kv_cache: layer 41: dev = CPU +llama_kv_cache: layer 42: filtered +llama_kv_cache: layer 43: filtered +llama_kv_cache: layer 44: filtered +llama_kv_cache: layer 45: filtered +llama_kv_cache: layer 46: filtered +llama_kv_cache: layer 47: dev = CPU +llama_kv_cache: reusing layers: +llama_kv_cache: - layer 0: no reuse +llama_kv_cache: - layer 1: no reuse +llama_kv_cache: - layer 2: no reuse +llama_kv_cache: - layer 3: no reuse +llama_kv_cache: - layer 4: no reuse +llama_kv_cache: - layer 5: no reuse +llama_kv_cache: - layer 6: no reuse +llama_kv_cache: - layer 7: no reuse +llama_kv_cache: - layer 8: no reuse +llama_kv_cache: - layer 9: no reuse +llama_kv_cache: - layer 10: no reuse +llama_kv_cache: - layer 11: no reuse +llama_kv_cache: - layer 12: no reuse +llama_kv_cache: - layer 13: no reuse +llama_kv_cache: - layer 14: no reuse +llama_kv_cache: - layer 15: no reuse +llama_kv_cache: - layer 16: no reuse +llama_kv_cache: - layer 17: no reuse +llama_kv_cache: - layer 18: no reuse +llama_kv_cache: - layer 19: no reuse +llama_kv_cache: - layer 20: no reuse +llama_kv_cache: - layer 21: no reuse +llama_kv_cache: - layer 22: no reuse +llama_kv_cache: - layer 23: no reuse +llama_kv_cache: - layer 24: no reuse +llama_kv_cache: - layer 25: no reuse +llama_kv_cache: - layer 26: no reuse +llama_kv_cache: - layer 27: no reuse +llama_kv_cache: - layer 28: no reuse +llama_kv_cache: - layer 29: no reuse +llama_kv_cache: - layer 30: no reuse +llama_kv_cache: - layer 31: no reuse +llama_kv_cache: - layer 32: no reuse +llama_kv_cache: - layer 33: no reuse +llama_kv_cache: - layer 34: no reuse +llama_kv_cache: - layer 35: no reuse +llama_kv_cache: - layer 36: no reuse +llama_kv_cache: - layer 37: no reuse +llama_kv_cache: - layer 38: no reuse +llama_kv_cache: - layer 39: no reuse +llama_kv_cache: - layer 40: no reuse +llama_kv_cache: - layer 41: no reuse +llama_kv_cache: - layer 42: no reuse +llama_kv_cache: - layer 43: no reuse +llama_kv_cache: - layer 44: no reuse +llama_kv_cache: - layer 45: no reuse +llama_kv_cache: - layer 46: no reuse +llama_kv_cache: - layer 47: no reuse +llama_kv_cache: CPU KV buffer size = 80.00 MiB +llama_kv_cache: size = 80.00 MiB ( 2048 cells, 8 layers, 1/1 seqs), K (f16): 16.00 MiB, V (f16): 64.00 MiB +llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 512 +llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 512 +llama_kv_cache_iswa: creating SWA KV cache, size = 2048 cells +llama_kv_cache: the V embeddings have different sizes across layers and FA is not enabled - padding V cache to 2048 +llama_kv_cache: layer 0: dev = CPU +llama_kv_cache: layer 1: dev = CPU +llama_kv_cache: layer 2: dev = CPU +llama_kv_cache: layer 3: dev = CPU +llama_kv_cache: layer 4: dev = CPU +llama_kv_cache: layer 5: filtered +llama_kv_cache: layer 6: dev = CPU +llama_kv_cache: layer 7: dev = CPU +llama_kv_cache: layer 8: dev = CPU +llama_kv_cache: layer 9: dev = CPU +llama_kv_cache: layer 10: dev = CPU +llama_kv_cache: layer 11: filtered +llama_kv_cache: layer 12: dev = CPU +llama_kv_cache: layer 13: dev = CPU +llama_kv_cache: layer 14: dev = CPU +llama_kv_cache: layer 15: dev = CPU +llama_kv_cache: layer 16: dev = CPU +llama_kv_cache: layer 17: filtered +llama_kv_cache: layer 18: dev = CPU +llama_kv_cache: layer 19: dev = CPU +llama_kv_cache: layer 20: dev = CPU +llama_kv_cache: layer 21: dev = CPU +llama_kv_cache: layer 22: dev = CPU +llama_kv_cache: layer 23: filtered +llama_kv_cache: layer 24: dev = CPU +llama_kv_cache: layer 25: dev = CPU +llama_kv_cache: layer 26: dev = CPU +llama_kv_cache: layer 27: dev = CPU +llama_kv_cache: layer 28: dev = CPU +llama_kv_cache: layer 29: filtered +llama_kv_cache: layer 30: dev = CPU +llama_kv_cache: layer 31: dev = CPU +llama_kv_cache: layer 32: dev = CPU +llama_kv_cache: layer 33: dev = CPU +llama_kv_cache: layer 34: dev = CPU +llama_kv_cache: layer 35: filtered +llama_kv_cache: layer 36: dev = CPU +llama_kv_cache: layer 37: dev = CPU +llama_kv_cache: layer 38: dev = CPU +llama_kv_cache: layer 39: dev = CPU +llama_kv_cache: layer 40: dev = CPU +llama_kv_cache: layer 41: filtered +llama_kv_cache: layer 42: dev = CPU +llama_kv_cache: layer 43: dev = CPU +llama_kv_cache: layer 44: dev = CPU +llama_kv_cache: layer 45: dev = CPU +llama_kv_cache: layer 46: dev = CPU +llama_kv_cache: layer 47: filtered +llama_kv_cache: reusing layers: +llama_kv_cache: - layer 0: no reuse +llama_kv_cache: - layer 1: no reuse +llama_kv_cache: - layer 2: no reuse +llama_kv_cache: - layer 3: no reuse +llama_kv_cache: - layer 4: no reuse +llama_kv_cache: - layer 5: no reuse +llama_kv_cache: - layer 6: no reuse +llama_kv_cache: - layer 7: no reuse +llama_kv_cache: - layer 8: no reuse +llama_kv_cache: - layer 9: no reuse +llama_kv_cache: - layer 10: no reuse +llama_kv_cache: - layer 11: no reuse +llama_kv_cache: - layer 12: no reuse +llama_kv_cache: - layer 13: no reuse +llama_kv_cache: - layer 14: no reuse +llama_kv_cache: - layer 15: no reuse +llama_kv_cache: - layer 16: no reuse +llama_kv_cache: - layer 17: no reuse +llama_kv_cache: - layer 18: no reuse +llama_kv_cache: - layer 19: no reuse +llama_kv_cache: - layer 20: no reuse +llama_kv_cache: - layer 21: no reuse +llama_kv_cache: - layer 22: no reuse +llama_kv_cache: - layer 23: no reuse +llama_kv_cache: - layer 24: no reuse +llama_kv_cache: - layer 25: no reuse +llama_kv_cache: - layer 26: no reuse +llama_kv_cache: - layer 27: no reuse +llama_kv_cache: - layer 28: no reuse +llama_kv_cache: - layer 29: no reuse +llama_kv_cache: - layer 30: no reuse +llama_kv_cache: - layer 31: no reuse +llama_kv_cache: - layer 32: no reuse +llama_kv_cache: - layer 33: no reuse +llama_kv_cache: - layer 34: no reuse +llama_kv_cache: - layer 35: no reuse +llama_kv_cache: - layer 36: no reuse +llama_kv_cache: - layer 37: no reuse +llama_kv_cache: - layer 38: no reuse +llama_kv_cache: - layer 39: no reuse +llama_kv_cache: - layer 40: no reuse +llama_kv_cache: - layer 41: no reuse +llama_kv_cache: - layer 42: no reuse +llama_kv_cache: - layer 43: no reuse +llama_kv_cache: - layer 44: no reuse +llama_kv_cache: - layer 45: no reuse +llama_kv_cache: - layer 46: no reuse +llama_kv_cache: - layer 47: no reuse +llama_kv_cache: CPU KV buffer size = 640.00 MiB +llama_kv_cache: size = 640.00 MiB ( 2048 cells, 40 layers, 1/1 seqs), K (f16): 320.00 MiB, V (f16): 320.00 MiB +llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256 +llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256 +llama_context: enumerating backends +llama_context: backend_ptrs.size() = 1 +sched_reserve: reserving ... +sched_reserve: max_nodes = 5344 +sched_reserve: reserving full memory module +sched_reserve: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 1 +resolve_fused_ops: resolving fused Gated Delta Net support: +graph_reserve: reserving a graph for ubatch with n_tokens = 1, n_seqs = 1, n_outputs = 1 +resolve_fused_ops: fused Gated Delta Net (autoregressive) enabled +graph_reserve: reserving a graph for ubatch with n_tokens = 16, n_seqs = 1, n_outputs = 16 +resolve_fused_ops: fused Gated Delta Net (chunked) enabled +resolve_fused_ops: resolving fused Lightning Indexer support: +graph_reserve: reserving a graph for ubatch with n_tokens = 1, n_seqs = 1, n_outputs = 1 +resolve_fused_ops: Lightning Indexer 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 = 544.51 MiB +sched_reserve: graph nodes = 2218 +sched_reserve: graph splits = 1 +sched_reserve: reserve took 23.14 ms, sched copies = 1 +CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 | +Model metadata: {'gemma4.attention.layer_norm_rms_epsilon': '0.000001', 'general.sampling.top_p': '0.950000', 'general.name': 'Google_Gemma 4 12B It', 'general.architecture': 'gemma4', 'tokenizer.ggml.mask_token_id': '4', 'general.type': 'model', 'general.sampling.top_k': '64', 'general.sampling.temp': '1.000000', 'general.basename': 'google_gemma-4', 'general.finetune': 'it', 'general.size_label': '12B', 'gemma4.block_count': '48', 'tokenizer.ggml.add_space_prefix': 'false', 'gemma4.rope.dimension_count_swa': '256', 'gemma4.context_length': '262144', 'gemma4.embedding_length': '3840', 'gemma4.attention.sliding_window': '1024', 'gemma4.feed_forward_length': '15360', 'gemma4.attention.key_length_swa': '256', 'gemma4.attention.head_count': '16', 'gemma4.rope.freq_base': '1000000.000000', 'gemma4.rope.dimension_count': '512', 'gemma4.rope.freq_base_swa': '10000.000000', 'gemma4.attention.key_length': '512', 'gemma4.attention.value_length': '512', 'gemma4.final_logit_softcapping': '30.000000', 'tokenizer.ggml.padding_token_id': '0', 'gemma4.attention.shared_kv_layers': '0', 'gemma4.embedding_length_per_layer_input': '0', 'gemma4.attention.value_length_swa': '256', 'tokenizer.ggml.model': 'gemma4', 'general.quantization_version': '2', 'tokenizer.ggml.bos_token_id': '2', 'general.file_type': '15', 'tokenizer.ggml.eos_token_id': '1', 'tokenizer.ggml.unknown_token_id': '3', 'tokenizer.chat_template': '{#\n Template: Google Gemma 4 Canonical Chat Template\n Author: Google Gemma Engineering Team\n Published: 2026-07-09\n Context: Fixed tool-calling loops, turn closures, and thinking content-ordering.\n#}\n{%- macro format_parameters(properties, required, filter_keys=false) -%}\n {%- set standard_keys = [\'description\', \'type\', \'properties\', \'required\', \'nullable\'] -%}\n {%- set ns = namespace(found_first=false) -%}\n {%- for key, value in properties | dictsort -%}\n {%- set add_comma = false -%}\n {%- if not filter_keys or key not in standard_keys -%}\n {%- if ns.found_first %},{% endif -%}\n {%- set ns.found_first = true -%}\n {{ key }}:{\n {%- if value[\'description\'] -%}\n description:<|"|>{{ value[\'description\'] }}<|"|>\n {%- set add_comma = true -%}\n {%- endif -%}\n {%- if value[\'type\'] | upper == \'STRING\' -%}\n + {%- if value[\'enum\'] -%}\n {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n enum:{{ format_argument(value[\'enum\']) }}\n {%- endif -%}\n {%- elif value[\'type\'] | upper == \'ARRAY\' -%}\n {%- if value[\'items\'] is mapping and value[\'items\'] -%}\n {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n items:{\n {%- set ns_items = namespace(found_first=false) -%}\n {%- for item_key, item_value in value[\'items\'] | dictsort -%}\n {%- if item_value is not none -%}\n + {%- if ns_items.found_first %},{% endif -%}\n + {%- set ns_items.found_first = true -%}\n {%- if item_key == \'properties\' -%}\n properties:{\n {%- if item_value is mapping -%}\n + {{- format_parameters(item_value, value[\'items\'][\'required\'] | default([])) -}}\n {%- endif -%}\n }\n {%- elif item_key == \'required\' -%}\n required:[\n + {%- for req_item in item_value -%}\n + <|"|>{{- req_item -}}<|"|>\n {%- if not loop.last %},{% endif -%}\n {%- endfor -%}\n ]\n {%- elif item_key == \'type\' -%}\n {%- if item_value is string -%}\n type:{{ format_argument(item_value | upper) }}\n {%- else -%}\n + type:{{ format_argument(item_value | map(\'upper\') | list) }}\n + {%- endif -%}\n {%- else -%}\n {{ item_key }}:{{ format_argument(item_value) }}\n {%- endif -%}\n {%- endif -%}\n {%- endfor -%}\n }\n {%- endif -%}\n {%- endif -%}\n {%- if value[\'nullable\'] %}\n {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n nullable:true\n {%- endif -%}\n + {%- if value[\'type\'] | upper == \'OBJECT\' -%}\n {%- if value[\'properties\'] is defined and value[\'properties\'] is mapping -%}\n + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n properties:{\n {{- format_parameters(value[\'properties\'], value[\'required\'] | default([])) -}}\n + }\n {%- elif value is mapping -%}\n {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n + properties:{\n {{- format_parameters(value, value[\'required\'] | default([]), filter_keys=true) -}}\n }\n {%- endif -%}\n {%- if value[\'required\'] -%}\n {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n required:[\n {%- for item in value[\'required\'] | default([]) -%}\n <|"|>{{- item -}}<|"|>\n {%- if not loop.last %},{% endif -%}\n + {%- endfor -%}\n ]\n {%- endif -%}\n {%- endif -%}\n {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n type:<|"|>{{ value[\'type\'] | upper }}<|"|>}\n {%- endif -%}\n {%- endfor -%}\n{%- endmacro -%}\n{%- macro format_function_declaration(tool_data) -%}\n declaration:{{- tool_data[\'function\'][\'name\'] -}}{description:<|"|>{{- tool_data[\'function\'][\'description\'] -}}<|"|>\n {%- set params = tool_data[\'function\'][\'parameters\'] -%}\n {%- if params -%}\n ,parameters:{\n {%- if params[\'properties\'] -%}\n properties:{ {{- format_parameters(params[\'properties\'], params[\'required\']) -}} },\n {%- endif -%}\n {%- if params[\'required\'] -%}\n required:[\n {%- for item in params[\'required\'] -%}\n <|"|>{{- item -}}<|"|>\n {{- \',\' if not loop.last -}}\n {%- endfor -%}\n ],\n {%- endif -%}\n {%- if params[\'type\'] -%}\n type:<|"|>{{- params[\'type\'] | upper -}}<|"|>}\n {%- endif -%}\n {%- endif -%}\n {%- if \'response\' in tool_data[\'function\'] -%}\n {%- set response_declaration = tool_data[\'function\'][\'response\'] -%}\n ,response:{\n {%- if response_declaration[\'description\'] -%}\n description:<|"|>{{- response_declaration[\'description\'] -}}<|"|>,\n {%- endif -%}\n {%- if response_declaration[\'type\'] | upper == \'OBJECT\' -%}\n type:<|"|>{{- response_declaration[\'type\'] | upper -}}<|"|>}\n {%- endif -%}\n {%- endif -%}\n }\n{%- endmacro -%}\n{%- macro format_argument(argument, escape_keys=True) -%}\n {%- if argument is none -%}\n {{- \'null\' -}}\n {%- elif argument is string -%}\n {{- \'<|"|>\' + argument + \'<|"|>\' -}}\n {%- elif argument is boolean -%}\n {{- \'true\' if argument else \'false\' -}}\n {%- elif argument is mapping -%}\n {{- \'{\' -}}\n {%- set ns = namespace(found_first=false) -%}\n {%- for key, value in argument | dictsort -%}\n {%- if ns.found_first %},{% endif -%}\n {%- set ns.found_first = true -%}\n {%- if escape_keys -%}\n {{- \'<|"|>\' + key + \'<|"|>\' -}}\n {%- else -%}\n {{- key -}}\n {%- endif -%}\n :{{- format_argument(value, escape_keys=escape_keys) -}}\n {%- endfor -%}\n {{- \'}\' -}}\n {%- elif argument is sequence -%}\n {{- \'[\' -}}\n {%- for item in argument -%}\n {{- format_argument(item, escape_keys=escape_keys) -}}\n {%- if not loop.last %},{% endif -%}\n {%- endfor -%}\n {{- \']\' -}}\n {%- else -%}\n {{- argument -}}\n {%- endif -%}\n{%- endmacro -%}\n{%- macro strip_thinking(text) -%}\n {%- set ns = namespace(result=\'\') -%}\n {%- for part in text.split(\'\') -%}\n {%- if \'<|channel>\' in part -%}\n {%- set ns.result = ns.result + part.split(\'<|channel>\')[0] -%}\n {%- else -%}\n {%- set ns.result = ns.result + part -%}\n {%- endif -%}\n {%- endfor -%}\n {{- ns.result | trim -}}\n{%- endmacro -%}\n\n{%- macro format_tool_response_block(tool_name, response) -%}\n {{- \'<|tool_response>\' -}}\n {%- if response is mapping -%}\n {{- \'response:\' + tool_name + \'{\' -}}\n {%- for key, value in response | dictsort -%}\n + {{- key -}}:{{- format_argument(value, escape_keys=False) -}}\n {%- if not loop.last %},{% endif -%}\n {%- endfor -%}\n {{- \'}\' -}}\n {%- else -%}\n {{- \'response:\' + tool_name + \'{value:\' + format_argument(response, escape_keys=False) + \'}\' -}}\n {%- endif -%}\n {{- \'\' -}}\n{%- endmacro -%}\n\n{#- ===== SETUP ===== -#}\n{%- set ns = namespace(prev_message_type=None, prev_non_tool_role=None) -%}\n{%- set loop_messages = messages -%}\n{%- set enable_thinking = enable_thinking | default(false) -%}\n{%- set preserve_thinking = preserve_thinking | default(false) -%}\n{{- bos_token -}}\n{#- Handle System/Tool Definitions Block -#}\n{%- if enable_thinking or tools or (messages and messages[0][\'role\'] in [\'system\', \'developer\']) -%}\n {{- \'<|turn>system\\n\' -}}\n {#- Inject Thinking token at the very top of the FIRST system turn -#}\n {%- if enable_thinking -%}\n {{- \'<|think|>\\n\' -}}\n {%- set ns.prev_message_type = \'think\' -%}\n {%- endif -%}\n {%- if messages and messages[0][\'role\'] in [\'system\', \'developer\'] -%}\n {%- if messages[0][\'content\'] is string -%}\n {{- messages[0][\'content\'] | trim -}}\n {%- elif messages[0][\'content\'] is sequence -%}\n {%- for item in messages[0][\'content\'] -%}\n {{- item[\'text\'] | trim + \' \'-}}\n {%- endfor -%}\n {%- endif -%}\n {%- set loop_messages = messages[1:] -%}\n {%- endif -%}\n {%- if tools -%}\n {%- for tool in tools %}\n {{- \'<|tool>\' -}}\n {{- format_function_declaration(tool) | trim -}}\n {{- \'\' -}}\n {%- endfor %}\n {%- set ns.prev_message_type = \'tool\' -%}\n {%- endif -%}\n {{- \'\\n\' -}}\n{%- endif %}\n\n{#- Pre-scan: find last user message index for reasoning guard -#}\n{%- set ns_turn = namespace(last_user_idx=-1) -%}\n{%- for i in range(loop_messages | length) -%}\n {%- if loop_messages[i][\'role\'] == \'user\' -%}\n {%- set ns_turn.last_user_idx = i -%}\n {%- endif -%}\n{%- endfor -%}\n\n{#- Loop through messages -#}\n{%- for message in loop_messages -%}\n {%- if message[\'role\'] != \'tool\' -%}\n {%- set ns.prev_message_type = None -%}\n {%- set role = \'model\' if message[\'role\'] == \'assistant\' else message[\'role\'] -%}\n {#- Detect continuation using tracked state — O(1) instead of O(n) backward scan -#}\n {%- set continue_same_model_turn = (role == \'model\' and ns.prev_non_tool_role == \'assistant\') -%}\n {%- if not continue_same_model_turn -%}\n {{- \'<|turn>\' + role + \'\\n\' }}\n\n {%- endif -%}\n\n {#- Render reasoning/reasoning_content as thinking channel -#}\n {%- set thinking_text = message.get(\'reasoning\') or message.get(\'reasoning_content\') -%}\n {%- set thinking_gate = (loop.index0 > ns_turn.last_user_idx) or (preserve_thinking and message.get(\'tool_calls\')) -%}\n {%- if thinking_text and thinking_gate -%}\n {{- \'<|channel>thought\\n\' + thinking_text + \'\\n\' -}}\n {%- endif -%}\n\n {%- if message.get(\'tool_calls\') -%}\n {%- for tool_call in message.get(\'tool_calls\') -%}\n {%- set function = tool_call[\'function\'] -%}\n {{- \'<|tool_call>call:\' + function[\'name\'] + \'{\' -}}\n {%- if function[\'arguments\'] is mapping -%}\n + {%- set ns_args = namespace(found_first=false) -%}\n + {%- for key, value in function[\'arguments\'] | dictsort -%}\n {%- if ns_args.found_first %},{% endif -%}\n + {%- set ns_args.found_first = true -%}\n + {{- key -}}:{{- format_argument(value, escape_keys=False) -}}\n + {%- endfor -%}\n {%- elif function[\'arguments\'] is none -%}\n {%- else -%}\n {{- raise_exception(\n "chat_template: tool_calls[].function.arguments must be a "\n "JSON object (mapping), not a string. Deserialize arguments "\n "before passing to the template."\n ) -}}\n {%- endif -%}\n {{- \'}\' -}}\n {%- endfor -%}\n {%- set ns.prev_message_type = \'tool_call\' -%}\n {%- endif -%}\n\n {%- set ns_tr_out = namespace(flag=false) -%}\n {%- if message.get(\'tool_responses\') -%}\n {#- Legacy: tool_responses embedded on the assistant message (Google/Gemma native) -#}\n {%- for tool_response in message.get(\'tool_responses\') -%}\n + {{- format_tool_response_block(tool_response[\'name\'] | default(\'unknown\', true), tool_response[\'response\']) -}}\n {%- set ns_tr_out.flag = true -%}\n {%- set ns.prev_message_type = \'tool_response\' -%}\n {%- endfor -%}\n {%- elif message.get(\'tool_calls\') -%}\n {#- OpenAI Chat Completions: forward-scan consecutive role:tool messages -#}\n {%- set ns_tool_scan = namespace(stopped=false) -%}\n {%- for k in range(loop.index0 + 1, loop_messages | length) -%}\n {%- if ns_tool_scan.stopped -%}\n {%- elif loop_messages[k][\'role\'] != \'tool\' -%}\n {%- set ns_tool_scan.stopped = true -%}\n {%- else -%}\n {%- set follow = loop_messages[k] -%}\n {#- Resolve tool_call_id to function name -#}\n + {%- set ns_tname = namespace(name=follow.get(\'name\') or \'unknown\') -%}\n {%- for tc in message.get(\'tool_calls\') -%}\n {%- if tc.get(\'id\') == follow.get(\'tool_call_id\') -%}\n {%- set ns_tname.name = tc[\'function\'][\'name\'] -%}\n {%- endif -%}\n +{%- endfor -%}\n {#- Handle content as string or content-parts array -#}\n {%- set tool_body = follow.get(\'content\') -%}\n {%- if tool_body is string -%}\n + {{- format_tool_response_block(ns_tname.name, tool_body) -}}\n {%- elif tool_body is sequence and tool_body is not string -%}\n {%- set ns_txt = namespace(s=\'\') -%}\n {%- for part in tool_body -%}\n + {%- if part.get(\'type\') == \'text\' -%}\n + {%- set ns_txt.s = ns_txt.s + (part.get(\'text\') | default(\'\')) -%}\n {%- endif -%}\n {%- endfor -%}\n {{- format_tool_response_block(ns_tname.name, ns_txt.s) -}}\n {%- for part in tool_body -%}\n {%- if part.get(\'type\') in [\'image\', \'image_url\'] -%}\n {{- \'<|image|>\' -}}\n + {%- elif part.get(\'type\') in [\'audio\', \'input_audio\'] -%}\n {{- \'<|audio|>\' -}}\n + {%- elif part.get(\'type\') == \'video\' -%}\n + {{- \'<|video|>\' -}}\n {%- endif -%}\n {%- endfor -%}\n {%- else -%}\n {{- format_tool_response_block(ns_tname.name, tool_body) -}}\n {%- endif -%}\n +{%- set ns_tr_out.flag = true -%}\n {%- set ns.prev_message_type = \'tool_response\' -%}\n {%- endif -%}\n + {%- endfor -%}\n {%- endif -%}\n\n {%- set captured_content -%}\n {%- if message.get(\'content\') is string -%}\n + {%- if role == \'model\' -%}\n {{- strip_thinking(message[\'content\']) -}}\n {%- else -%}\n {{- message[\'content\'] | trim -}}\n {%- endif -%}\n {%- elif message.get(\'content\') is sequence -%}\n {%- for item in message[\'content\'] -%}\n {%- if item.get(\'type\') == \'text\' -%}\n {%- if role == \'model\' -%}\n + {{- strip_thinking(item[\'text\']) -}}\n {%- else -%}\n {{- item[\'text\'] | trim -}}\n + {%- endif -%}\n {%- elif item.get(\'type\') in [\'image\', \'image_url\'] -%}\n {{- \'<|image|>\' -}}\n {%- elif item.get(\'type\') in [\'audio\', \'input_audio\'] -%}\n {{- \'<|audio|>\' -}}\n {%- elif item.get(\'type\') == \'video\' -%}\n {{- \'<|video|>\' -}}\n {%- endif -%}\n {%- endfor -%}\n {%- endif -%}\n {%- endset -%}\n\n {{- captured_content -}}\n {%- set has_content = captured_content | trim | length > 0 -%}\n\n {#- Forward-scan: find next non-tool message role for continuation detection -#}\n {%- set next_nt = namespace(role=None, found=false) -%}\n {%- for j in range(loop.index0 + 1, loop_messages | length) -%}\n {%- if not next_nt.found -%}\n {%- if loop_messages[j][\'role\'] != \'tool\' -%}\n {%- set next_nt.role = loop_messages[j][\'role\'] -%}\n {%- set next_nt.found = true -%}\n {%- endif -%}\n {%- endif -%}\n {%- endfor -%}\n\n {%- set continues_into_next = (\n role == \'model\'\n and next_nt.role == \'assistant\'\n and (not message.get(\'tool_calls\') or ns_tr_out.flag)\n ) -%}\n\n {%- if ns.prev_message_type == \'tool_call\' and not ns_tr_out.flag -%}\n {{- \'<|tool_response>\' -}}\n {%- elif continues_into_next -%}\n {%- elif not (ns_tr_out.flag and not has_content and not next_nt.found) -%}\n {{- \'\\n\' -}}\n {%- endif -%}\n\n {#- Track previous non-tool role for next iteration (avoids O(n) backward scan) -#}\n {%- set ns.prev_non_tool_role = message[\'role\'] -%}\n {%- endif -%}\n{%- endfor -%}\n\n{%- if add_generation_prompt -%}\n {%- if ns.prev_message_type != \'tool_response\' and ns.prev_message_type != \'tool_call\' -%}\n {{- \'<|turn>model\\n\' -}}\n {%- if not enable_thinking -%}\n {{- \'<|channel>thought\\n\' -}}\n {%- endif -%}\n {%- elif ns.prev_message_type == \'tool_response\' and enable_thinking -%}\n {{- \'<|channel>thought\\n\' -}}\n {%- endif -%}\n{%- endif -%}', 'tokenizer.ggml.add_bos_token': 'true'} +Available chat formats from metadata: chat_template.default +Using gguf chat template: {# + Template: Google Gemma 4 Canonical Chat Template + Author: Google Gemma Engineering Team + Published: 2026-07-09 + Context: Fixed tool-calling loops, turn closures, and thinking content-ordering. +#} +{%- macro format_parameters(properties, required, filter_keys=false) -%} + {%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%} + {%- set ns = namespace(found_first=false) -%} + {%- for key, value in properties | dictsort -%} + {%- set add_comma = false -%} + {%- if not filter_keys or key not in standard_keys -%} + {%- if ns.found_first %},{% endif -%} + {%- set ns.found_first = true -%} + {{ key }}:{ + {%- if value['description'] -%} + description:<|"|>{{ value['description'] }}<|"|> + {%- set add_comma = true -%} + {%- endif -%} + {%- if value['type'] | upper == 'STRING' -%} + {%- if value['enum'] -%} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + enum:{{ format_argument(value['enum']) }} + {%- endif -%} + {%- elif value['type'] | upper == 'ARRAY' -%} + {%- if value['items'] is mapping and value['items'] -%} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + items:{ + {%- set ns_items = namespace(found_first=false) -%} + {%- for item_key, item_value in value['items'] | dictsort -%} + {%- if item_value is not none -%} + {%- if ns_items.found_first %},{% endif -%} + {%- set ns_items.found_first = true -%} + {%- if item_key == 'properties' -%} + properties:{ + {%- if item_value is mapping -%} + {{- format_parameters(item_value, value['items']['required'] | default([])) -}} + {%- endif -%} + } + {%- elif item_key == 'required' -%} + required:[ + {%- for req_item in item_value -%} + <|"|>{{- req_item -}}<|"|> + {%- if not loop.last %},{% endif -%} + {%- endfor -%} + ] + {%- elif item_key == 'type' -%} + {%- if item_value is string -%} + type:{{ format_argument(item_value | upper) }} + {%- else -%} + type:{{ format_argument(item_value | map('upper') | list) }} + {%- endif -%} + {%- else -%} + {{ item_key }}:{{ format_argument(item_value) }} + {%- endif -%} + {%- endif -%} + {%- endfor -%} + } + {%- endif -%} + {%- endif -%} + {%- if value['nullable'] %} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + nullable:true + {%- endif -%} + {%- if value['type'] | upper == 'OBJECT' -%} + {%- if value['properties'] is defined and value['properties'] is mapping -%} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + properties:{ + {{- format_parameters(value['properties'], value['required'] | default([])) -}} + } + {%- elif value is mapping -%} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + properties:{ + {{- format_parameters(value, value['required'] | default([]), filter_keys=true) -}} + } + {%- endif -%} + {%- if value['required'] -%} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + required:[ + {%- for item in value['required'] | default([]) -%} + <|"|>{{- item -}}<|"|> + {%- if not loop.last %},{% endif -%} + {%- endfor -%} + ] + {%- endif -%} + {%- endif -%} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + type:<|"|>{{ value['type'] | upper }}<|"|>} + {%- endif -%} + {%- endfor -%} +{%- endmacro -%} +{%- macro format_function_declaration(tool_data) -%} + declaration:{{- tool_data['function']['name'] -}}{description:<|"|>{{- tool_data['function']['description'] -}}<|"|> + {%- set params = tool_data['function']['parameters'] -%} + {%- if params -%} + ,parameters:{ + {%- if params['properties'] -%} + properties:{ {{- format_parameters(params['properties'], params['required']) -}} }, + {%- endif -%} + {%- if params['required'] -%} + required:[ + {%- for item in params['required'] -%} + <|"|>{{- item -}}<|"|> + {{- ',' if not loop.last -}} + {%- endfor -%} + ], + {%- endif -%} + {%- if params['type'] -%} + type:<|"|>{{- params['type'] | upper -}}<|"|>} + {%- endif -%} + {%- endif -%} + {%- if 'response' in tool_data['function'] -%} + {%- set response_declaration = tool_data['function']['response'] -%} + ,response:{ + {%- if response_declaration['description'] -%} + description:<|"|>{{- response_declaration['description'] -}}<|"|>, + {%- endif -%} + {%- if response_declaration['type'] | upper == 'OBJECT' -%} + type:<|"|>{{- response_declaration['type'] | upper -}}<|"|>} + {%- endif -%} + {%- endif -%} + } +{%- endmacro -%} +{%- macro format_argument(argument, escape_keys=True) -%} + {%- if argument is none -%} + {{- 'null' -}} + {%- elif argument is string -%} + {{- '<|"|>' + argument + '<|"|>' -}} + {%- elif argument is boolean -%} + {{- 'true' if argument else 'false' -}} + {%- elif argument is mapping -%} + {{- '{' -}} + {%- set ns = namespace(found_first=false) -%} + {%- for key, value in argument | dictsort -%} + {%- if ns.found_first %},{% endif -%} + {%- set ns.found_first = true -%} + {%- if escape_keys -%} + {{- '<|"|>' + key + '<|"|>' -}} + {%- else -%} + {{- key -}} + {%- endif -%} + :{{- format_argument(value, escape_keys=escape_keys) -}} + {%- endfor -%} + {{- '}' -}} + {%- elif argument is sequence -%} + {{- '[' -}} + {%- for item in argument -%} + {{- format_argument(item, escape_keys=escape_keys) -}} + {%- if not loop.last %},{% endif -%} + {%- endfor -%} + {{- ']' -}} + {%- else -%} + {{- argument -}} + {%- endif -%} +{%- endmacro -%} +{%- macro strip_thinking(text) -%} + {%- set ns = namespace(result='') -%} + {%- for part in text.split('') -%} + {%- if '<|channel>' in part -%} + {%- set ns.result = ns.result + part.split('<|channel>')[0] -%} + {%- else -%} + {%- set ns.result = ns.result + part -%} + {%- endif -%} + {%- endfor -%} + {{- ns.result | trim -}} +{%- endmacro -%} + +{%- macro format_tool_response_block(tool_name, response) -%} + {{- '<|tool_response>' -}} + {%- if response is mapping -%} + {{- 'response:' + tool_name + '{' -}} + {%- for key, value in response | dictsort -%} + {{- key -}}:{{- format_argument(value, escape_keys=False) -}} + {%- if not loop.last %},{% endif -%} + {%- endfor -%} + {{- '}' -}} + {%- else -%} + {{- 'response:' + tool_name + '{value:' + format_argument(response, escape_keys=False) + '}' -}} + {%- endif -%} + {{- '' -}} +{%- endmacro -%} + +{#- ===== SETUP ===== -#} +{%- set ns = namespace(prev_message_type=None, prev_non_tool_role=None) -%} +{%- set loop_messages = messages -%} +{%- set enable_thinking = enable_thinking | default(false) -%} +{%- set preserve_thinking = preserve_thinking | default(false) -%} +{{- bos_token -}} +{#- Handle System/Tool Definitions Block -#} +{%- if enable_thinking or tools or (messages and messages[0]['role'] in ['system', 'developer']) -%} + {{- '<|turn>system\n' -}} + {#- Inject Thinking token at the very top of the FIRST system turn -#} + {%- if enable_thinking -%} + {{- '<|think|>\n' -}} + {%- set ns.prev_message_type = 'think' -%} + {%- endif -%} + {%- if messages and messages[0]['role'] in ['system', 'developer'] -%} + {%- if messages[0]['content'] is string -%} + {{- messages[0]['content'] | trim -}} + {%- elif messages[0]['content'] is sequence -%} + {%- for item in messages[0]['content'] -%} + {{- item['text'] | trim + ' '-}} + {%- endfor -%} + {%- endif -%} + {%- set loop_messages = messages[1:] -%} + {%- endif -%} + {%- if tools -%} + {%- for tool in tools %} + {{- '<|tool>' -}} + {{- format_function_declaration(tool) | trim -}} + {{- '' -}} + {%- endfor %} + {%- set ns.prev_message_type = 'tool' -%} + {%- endif -%} + {{- '\n' -}} +{%- endif %} + +{#- Pre-scan: find last user message index for reasoning guard -#} +{%- set ns_turn = namespace(last_user_idx=-1) -%} +{%- for i in range(loop_messages | length) -%} + {%- if loop_messages[i]['role'] == 'user' -%} + {%- set ns_turn.last_user_idx = i -%} + {%- endif -%} +{%- endfor -%} + +{#- Loop through messages -#} +{%- for message in loop_messages -%} + {%- if message['role'] != 'tool' -%} + {%- set ns.prev_message_type = None -%} + {%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%} + {#- Detect continuation using tracked state — O(1) instead of O(n) backward scan -#} + {%- set continue_same_model_turn = (role == 'model' and ns.prev_non_tool_role == 'assistant') -%} + {%- if not continue_same_model_turn -%} + {{- '<|turn>' + role + '\n' }} + + {%- endif -%} + + {#- Render reasoning/reasoning_content as thinking channel -#} + {%- set thinking_text = message.get('reasoning') or message.get('reasoning_content') -%} + {%- set thinking_gate = (loop.index0 > ns_turn.last_user_idx) or (preserve_thinking and message.get('tool_calls')) -%} + {%- if thinking_text and thinking_gate -%} + {{- '<|channel>thought\n' + thinking_text + '\n' -}} + {%- endif -%} + + {%- if message.get('tool_calls') -%} + {%- for tool_call in message.get('tool_calls') -%} + {%- set function = tool_call['function'] -%} + {{- '<|tool_call>call:' + function['name'] + '{' -}} + {%- if function['arguments'] is mapping -%} + {%- set ns_args = namespace(found_first=false) -%} + {%- for key, value in function['arguments'] | dictsort -%} + {%- if ns_args.found_first %},{% endif -%} + {%- set ns_args.found_first = true -%} + {{- key -}}:{{- format_argument(value, escape_keys=False) -}} + {%- endfor -%} + {%- elif function['arguments'] is none -%} + {%- else -%} + {{- raise_exception( + "chat_template: tool_calls[].function.arguments must be a " + "JSON object (mapping), not a string. Deserialize arguments " + "before passing to the template." + ) -}} + {%- endif -%} + {{- '}' -}} + {%- endfor -%} + {%- set ns.prev_message_type = 'tool_call' -%} + {%- endif -%} + + {%- set ns_tr_out = namespace(flag=false) -%} + {%- if message.get('tool_responses') -%} + {#- Legacy: tool_responses embedded on the assistant message (Google/Gemma native) -#} + {%- for tool_response in message.get('tool_responses') -%} + {{- format_tool_response_block(tool_response['name'] | default('unknown', true), tool_response['response']) -}} + {%- set ns_tr_out.flag = true -%} + {%- set ns.prev_message_type = 'tool_response' -%} + {%- endfor -%} + {%- elif message.get('tool_calls') -%} + {#- OpenAI Chat Completions: forward-scan consecutive role:tool messages -#} + {%- set ns_tool_scan = namespace(stopped=false) -%} + {%- for k in range(loop.index0 + 1, loop_messages | length) -%} + {%- if ns_tool_scan.stopped -%} + {%- elif loop_messages[k]['role'] != 'tool' -%} + {%- set ns_tool_scan.stopped = true -%} + {%- else -%} + {%- set follow = loop_messages[k] -%} + {#- Resolve tool_call_id to function name -#} + {%- set ns_tname = namespace(name=follow.get('name') or 'unknown') -%} + {%- for tc in message.get('tool_calls') -%} + {%- if tc.get('id') == follow.get('tool_call_id') -%} + {%- set ns_tname.name = tc['function']['name'] -%} + {%- endif -%} + {%- endfor -%} + {#- Handle content as string or content-parts array -#} + {%- set tool_body = follow.get('content') -%} + {%- if tool_body is string -%} + {{- format_tool_response_block(ns_tname.name, tool_body) -}} + {%- elif tool_body is sequence and tool_body is not string -%} + {%- set ns_txt = namespace(s='') -%} + {%- for part in tool_body -%} + {%- if part.get('type') == 'text' -%} + {%- set ns_txt.s = ns_txt.s + (part.get('text') | default('')) -%} + {%- endif -%} + {%- endfor -%} + {{- format_tool_response_block(ns_tname.name, ns_txt.s) -}} + {%- for part in tool_body -%} + {%- if part.get('type') in ['image', 'image_url'] -%} + {{- '<|image|>' -}} + {%- elif part.get('type') in ['audio', 'input_audio'] -%} + {{- '<|audio|>' -}} + {%- elif part.get('type') == 'video' -%} + {{- '<|video|>' -}} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {{- format_tool_response_block(ns_tname.name, tool_body) -}} + {%- endif -%} + {%- set ns_tr_out.flag = true -%} + {%- set ns.prev_message_type = 'tool_response' -%} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + + {%- set captured_content -%} + {%- if message.get('content') is string -%} + {%- if role == 'model' -%} + {{- strip_thinking(message['content']) -}} + {%- else -%} + {{- message['content'] | trim -}} + {%- endif -%} + {%- elif message.get('content') is sequence -%} + {%- for item in message['content'] -%} + {%- if item.get('type') == 'text' -%} + {%- if role == 'model' -%} + {{- strip_thinking(item['text']) -}} + {%- else -%} + {{- item['text'] | trim -}} + {%- endif -%} + {%- elif item.get('type') in ['image', 'image_url'] -%} + {{- '<|image|>' -}} + {%- elif item.get('type') in ['audio', 'input_audio'] -%} + {{- '<|audio|>' -}} + {%- elif item.get('type') == 'video' -%} + {{- '<|video|>' -}} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + {%- endset -%} + + {{- captured_content -}} + {%- set has_content = captured_content | trim | length > 0 -%} + + {#- Forward-scan: find next non-tool message role for continuation detection -#} + {%- set next_nt = namespace(role=None, found=false) -%} + {%- for j in range(loop.index0 + 1, loop_messages | length) -%} + {%- if not next_nt.found -%} + {%- if loop_messages[j]['role'] != 'tool' -%} + {%- set next_nt.role = loop_messages[j]['role'] -%} + {%- set next_nt.found = true -%} + {%- endif -%} + {%- endif -%} + {%- endfor -%} + + {%- set continues_into_next = ( + role == 'model' + and next_nt.role == 'assistant' + and (not message.get('tool_calls') or ns_tr_out.flag) + ) -%} + + {%- if ns.prev_message_type == 'tool_call' and not ns_tr_out.flag -%} + {{- '<|tool_response>' -}} + {%- elif continues_into_next -%} + {%- elif not (ns_tr_out.flag and not has_content and not next_nt.found) -%} + {{- '\n' -}} + {%- endif -%} + + {#- Track previous non-tool role for next iteration (avoids O(n) backward scan) -#} + {%- set ns.prev_non_tool_role = message['role'] -%} + {%- endif -%} +{%- endfor -%} + +{%- if add_generation_prompt -%} + {%- if ns.prev_message_type != 'tool_response' and ns.prev_message_type != 'tool_call' -%} + {{- '<|turn>model\n' -}} + {%- if not enable_thinking -%} + {{- '<|channel>thought\n' -}} + {%- endif -%} + {%- elif ns.prev_message_type == 'tool_response' and enable_thinking -%} + {{- '<|channel>thought\n' -}} + {%- endif -%} +{%- endif -%} +Using chat eos_token: +Using chat bos_token: +INFO: Started server process [46240] +INFO: Waiting for application startup. +INFO: Application startup complete. +INFO: Uvicorn running on http://localhost:8000 (Press CTRL+C to quit) +INFO: ::1:51306 - "GET / HTTP/1.1" 404 Not Found +INFO: ::1:51306 - "GET 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