Text Generation
Transformers
Safetensors
GGUF
English
gemma4
image-text-to-text
healthcare
clinical-decision-support
ncd
diabetes
hypertension
medical
unsloth
lora
conversational
Instructions to use samwell/ncd-gemma4-e4b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samwell/ncd-gemma4-e4b-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="samwell/ncd-gemma4-e4b-lora") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("samwell/ncd-gemma4-e4b-lora") model = AutoModelForMultimodalLM.from_pretrained("samwell/ncd-gemma4-e4b-lora", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use samwell/ncd-gemma4-e4b-lora with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf samwell/ncd-gemma4-e4b-lora:Q4_K_M # Run inference directly in the terminal: llama cli -hf samwell/ncd-gemma4-e4b-lora:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf samwell/ncd-gemma4-e4b-lora:Q4_K_M # Run inference directly in the terminal: llama cli -hf samwell/ncd-gemma4-e4b-lora:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf samwell/ncd-gemma4-e4b-lora:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf samwell/ncd-gemma4-e4b-lora:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf samwell/ncd-gemma4-e4b-lora:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf samwell/ncd-gemma4-e4b-lora:Q4_K_M
Use Docker
docker model run hf.co/samwell/ncd-gemma4-e4b-lora:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use samwell/ncd-gemma4-e4b-lora with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "samwell/ncd-gemma4-e4b-lora" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "samwell/ncd-gemma4-e4b-lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/samwell/ncd-gemma4-e4b-lora:Q4_K_M
- SGLang
How to use samwell/ncd-gemma4-e4b-lora with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "samwell/ncd-gemma4-e4b-lora" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "samwell/ncd-gemma4-e4b-lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "samwell/ncd-gemma4-e4b-lora" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "samwell/ncd-gemma4-e4b-lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use samwell/ncd-gemma4-e4b-lora with Ollama:
ollama run hf.co/samwell/ncd-gemma4-e4b-lora:Q4_K_M
- Unsloth Studio
How to use samwell/ncd-gemma4-e4b-lora with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for samwell/ncd-gemma4-e4b-lora to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for samwell/ncd-gemma4-e4b-lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for samwell/ncd-gemma4-e4b-lora to start chatting
- Pi
How to use samwell/ncd-gemma4-e4b-lora with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf samwell/ncd-gemma4-e4b-lora:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "samwell/ncd-gemma4-e4b-lora:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use samwell/ncd-gemma4-e4b-lora with Docker Model Runner:
docker model run hf.co/samwell/ncd-gemma4-e4b-lora:Q4_K_M
- Lemonade
How to use samwell/ncd-gemma4-e4b-lora with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull samwell/ncd-gemma4-e4b-lora:Q4_K_M
Run and chat with the model
lemonade run user.ncd-gemma4-e4b-lora-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use samwell/ncd-gemma4-e4b-lora with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf samwell/ncd-gemma4-e4b-lora:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default samwell/ncd-gemma4-e4b-lora:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use samwell/ncd-gemma4-e4b-lora with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf samwell/ncd-gemma4-e4b-lora:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "samwell/ncd-gemma4-e4b-lora:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Upload folder using huggingface_hub
Browse files- config.json +199 -0
- model.safetensors +3 -0
- tokenizer.json +2 -2
- tokenizer_config.json +3 -2
config.json
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| 1 |
+
{
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| 2 |
+
"architectures": [
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| 3 |
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"Gemma4ForConditionalGeneration"
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| 4 |
+
],
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| 5 |
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"audio_config": {
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| 6 |
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"_name_or_path": "",
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| 7 |
+
"architectures": null,
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| 8 |
+
"attention_chunk_size": 12,
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| 9 |
+
"attention_context_left": 13,
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| 10 |
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"attention_context_right": 0,
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| 11 |
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"attention_invalid_logits_value": -1000000000.0,
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| 12 |
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"attention_logit_cap": 50.0,
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| 13 |
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"chunk_size_feed_forward": 0,
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| 14 |
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"conv_kernel_size": 5,
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| 15 |
+
"torch_dtype": "bfloat16",
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| 16 |
+
"gradient_clipping": 10000000000.0,
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| 17 |
+
"hidden_act": "silu",
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| 18 |
+
"hidden_size": 1024,
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| 19 |
+
"id2label": {
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| 20 |
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"0": "LABEL_0",
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| 21 |
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"1": "LABEL_1"
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| 22 |
+
},
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| 23 |
+
"initializer_range": 0.02,
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| 24 |
+
"is_encoder_decoder": false,
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| 25 |
+
"label2id": {
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| 26 |
+
"LABEL_0": 0,
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| 27 |
+
"LABEL_1": 1
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},
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| 29 |
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"model_type": "gemma4_audio",
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| 30 |
+
"num_attention_heads": 8,
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| 31 |
+
"num_hidden_layers": 12,
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| 32 |
+
"output_attentions": false,
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| 33 |
+
"output_hidden_states": false,
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| 34 |
+
"output_proj_dims": 1536,
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| 35 |
+
"problem_type": null,
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| 36 |
+
"residual_weight": 0.5,
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| 37 |
+
"return_dict": true,
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| 38 |
+
"rms_norm_eps": 1e-06,
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| 39 |
+
"subsampling_conv_channels": [
|
| 40 |
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128,
|
| 41 |
+
32
|
| 42 |
+
],
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| 43 |
+
"use_clipped_linears": true
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| 44 |
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},
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| 45 |
+
"audio_token_id": 258881,
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| 46 |
+
"boa_token_id": 256000,
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| 47 |
+
"boi_token_id": 255999,
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| 48 |
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"bos_token_id": 2,
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| 49 |
+
"torch_dtype": "bfloat16",
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| 50 |
+
"eoa_token_id": 258883,
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| 51 |
+
"eoa_token_index": 258883,
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| 52 |
+
"eoi_token_id": 258882,
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| 53 |
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"eos_token_id": 1,
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| 54 |
+
"image_token_id": 258880,
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| 55 |
+
"initializer_range": 0.02,
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| 56 |
+
"model_name": "google/gemma-4-E4B-it",
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| 57 |
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"model_type": "gemma4",
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| 58 |
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"pad_token_id": 0,
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| 59 |
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"text_config": {
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| 60 |
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"attention_bias": false,
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| 61 |
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"attention_dropout": 0.0,
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| 62 |
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"attention_k_eq_v": false,
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| 63 |
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"bos_token_id": 2,
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| 64 |
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"torch_dtype": "bfloat16",
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| 65 |
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"enable_moe_block": false,
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| 66 |
+
"eos_token_id": 1,
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| 67 |
+
"expert_intermediate_size": null,
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| 68 |
+
"final_logit_softcapping": 30.0,
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| 69 |
+
"global_head_dim": 512,
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| 70 |
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"head_dim": 256,
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| 71 |
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"hidden_activation": "gelu_pytorch_tanh",
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| 72 |
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"hidden_size": 2560,
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| 73 |
+
"hidden_size_per_layer_input": 256,
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| 74 |
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"initializer_range": 0.02,
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| 75 |
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"intermediate_size": 10240,
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| 76 |
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"layer_types": [
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| 77 |
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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| 83 |
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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| 112 |
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"full_attention",
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"sliding_attention",
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| 114 |
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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| 118 |
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"full_attention"
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],
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| 120 |
+
"max_position_embeddings": 131072,
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| 121 |
+
"model_type": "gemma4_text",
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| 122 |
+
"moe_intermediate_size": null,
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| 123 |
+
"num_attention_heads": 8,
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| 124 |
+
"num_experts": null,
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| 125 |
+
"num_global_key_value_heads": null,
|
| 126 |
+
"num_hidden_layers": 42,
|
| 127 |
+
"num_key_value_heads": 2,
|
| 128 |
+
"num_kv_shared_layers": 18,
|
| 129 |
+
"pad_token_id": 0,
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| 130 |
+
"rms_norm_eps": 1e-06,
|
| 131 |
+
"rope_parameters": {
|
| 132 |
+
"full_attention": {
|
| 133 |
+
"partial_rotary_factor": 0.25,
|
| 134 |
+
"rope_theta": 1000000.0,
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| 135 |
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"rope_type": "proportional"
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| 136 |
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},
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| 137 |
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"sliding_attention": {
|
| 138 |
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"rope_theta": 10000.0,
|
| 139 |
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"rope_type": "default"
|
| 140 |
+
}
|
| 141 |
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},
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| 142 |
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"sliding_window": 512,
|
| 143 |
+
"tie_word_embeddings": true,
|
| 144 |
+
"top_k_experts": null,
|
| 145 |
+
"use_bidirectional_attention": null,
|
| 146 |
+
"use_cache": true,
|
| 147 |
+
"use_double_wide_mlp": false,
|
| 148 |
+
"vocab_size": 262144,
|
| 149 |
+
"vocab_size_per_layer_input": 262144
|
| 150 |
+
},
|
| 151 |
+
"tie_word_embeddings": true,
|
| 152 |
+
"unsloth_version": "2026.4.4",
|
| 153 |
+
"use_cache": false,
|
| 154 |
+
"video_token_id": 258884,
|
| 155 |
+
"vision_config": {
|
| 156 |
+
"_name_or_path": "",
|
| 157 |
+
"architectures": null,
|
| 158 |
+
"attention_bias": false,
|
| 159 |
+
"attention_dropout": 0.0,
|
| 160 |
+
"chunk_size_feed_forward": 0,
|
| 161 |
+
"default_output_length": 280,
|
| 162 |
+
"torch_dtype": "bfloat16",
|
| 163 |
+
"global_head_dim": 64,
|
| 164 |
+
"head_dim": 64,
|
| 165 |
+
"hidden_activation": "gelu_pytorch_tanh",
|
| 166 |
+
"hidden_size": 768,
|
| 167 |
+
"id2label": {
|
| 168 |
+
"0": "LABEL_0",
|
| 169 |
+
"1": "LABEL_1"
|
| 170 |
+
},
|
| 171 |
+
"initializer_range": 0.02,
|
| 172 |
+
"intermediate_size": 3072,
|
| 173 |
+
"is_encoder_decoder": false,
|
| 174 |
+
"label2id": {
|
| 175 |
+
"LABEL_0": 0,
|
| 176 |
+
"LABEL_1": 1
|
| 177 |
+
},
|
| 178 |
+
"max_position_embeddings": 131072,
|
| 179 |
+
"model_type": "gemma4_vision",
|
| 180 |
+
"num_attention_heads": 12,
|
| 181 |
+
"num_hidden_layers": 16,
|
| 182 |
+
"num_key_value_heads": 12,
|
| 183 |
+
"output_attentions": false,
|
| 184 |
+
"output_hidden_states": false,
|
| 185 |
+
"patch_size": 16,
|
| 186 |
+
"pooling_kernel_size": 3,
|
| 187 |
+
"position_embedding_size": 10240,
|
| 188 |
+
"problem_type": null,
|
| 189 |
+
"return_dict": true,
|
| 190 |
+
"rms_norm_eps": 1e-06,
|
| 191 |
+
"rope_parameters": {
|
| 192 |
+
"rope_theta": 100.0,
|
| 193 |
+
"rope_type": "default"
|
| 194 |
+
},
|
| 195 |
+
"standardize": false,
|
| 196 |
+
"use_clipped_linears": true
|
| 197 |
+
},
|
| 198 |
+
"vision_soft_tokens_per_image": 280
|
| 199 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a2284f32a30bde5ff8d396b708b2b76484095079d4ad67c9c603403564b1e40d
|
| 3 |
+
size 15992595852
|
tokenizer.json
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cc8d3a0ce36466ccc1278bf987df5f71db1719b9ca6b4118264f45cb627bfe0f
|
| 3 |
+
size 32169626
|
tokenizer_config.json
CHANGED
|
@@ -91,5 +91,6 @@
|
|
| 91 |
"str_token": "<|tool_response>",
|
| 92 |
"think_token": "<|think|>",
|
| 93 |
"tokenizer_class": "GemmaTokenizer",
|
| 94 |
-
"unk_token": "<unk>"
|
| 95 |
-
}
|
|
|
|
|
|
| 91 |
"str_token": "<|tool_response>",
|
| 92 |
"think_token": "<|think|>",
|
| 93 |
"tokenizer_class": "GemmaTokenizer",
|
| 94 |
+
"unk_token": "<unk>",
|
| 95 |
+
"chat_template": "{%- macro format_parameters(properties, required) -%}\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 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([])) -}}\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 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('<channel|>') -%}\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 {{- '<tool_response|>' -}}\n{%- endmacro -%}\n\n{%- set ns = namespace(prev_message_type=None) -%}\n{%- set loop_messages = messages -%}\n{{- bos_token -}}\n{#- Handle System/Tool Definitions Block -#}\n{%- if (enable_thinking is defined and enable_thinking) or tools or messages[0]['role'] in ['system', 'developer'] -%}\n {{- '<|turn>system\\n' -}}\n\n {#- Inject Thinking token at the very top of the FIRST system turn -#}\n {%- if enable_thinking is defined and enable_thinking -%}\n {{- '<|think|>\\n' -}}\n {%- set ns.prev_message_type = 'think' -%}\n {%- endif -%}\n\n {%- if messages[0]['role'] in ['system', 'developer'] -%}\n {{- messages[0]['content'] | trim -}}\n {%- set loop_messages = messages[1:] -%}\n {%- endif -%}\n\n {%- if tools -%}\n {%- for tool in tools %}\n {{- '<|tool>' -}}\n {{- format_function_declaration(tool) | trim -}}\n {{- '<tool|>' -}}\n {%- endfor %}\n {%- set ns.prev_message_type = 'tool' -%}\n {%- endif -%}\n\n {{- '<turn|>\\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: suppress duplicate <|turn>model when previous non-tool message was also assistant -#}\n {%- set prev_nt = namespace(role=None, found=false) -%}\n {%- if loop.index0 > 0 -%}\n {%- for j in range(loop.index0 - 1, -1, -1) -%}\n {%- if not prev_nt.found -%}\n {%- if loop_messages[j]['role'] != 'tool' -%}\n {%- set prev_nt.role = loop_messages[j]['role'] -%}\n {%- set prev_nt.found = true -%}\n {%- endif -%}\n {%- endif -%}\n {%- endfor -%}\n {%- endif -%}\n {%- set continue_same_model_turn = (role == 'model' and prev_nt.role == 'assistant') -%}\n {%- if not continue_same_model_turn -%}\n {{- '<|turn>' + role + '\\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 {%- if thinking_text and loop.index0 > ns_turn.last_user_idx and message.get('tool_calls') -%}\n {{- '<|channel>thought\\n' + thinking_text + '\\n<channel|>' -}}\n {%- endif -%}\n\n {%- if message['tool_calls'] -%}\n {%- for tool_call in message['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 string -%}\n {{- function['arguments'] -}}\n {%- endif -%}\n {{- '}<tool_call|>' -}}\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['tool_responses'] -%}\n {{- format_tool_response_block(tool_response['name'] | default('unknown'), 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') | default('unknown')) -%}\n {%- for tc in message['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 {%- 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 {%- if message['content'] is string -%}\n {%- if role == 'model' -%}\n {{- strip_thinking(message['content']) -}}\n {%- else -%}\n {{- message['content'] | trim -}}\n {%- endif -%}\n {%- elif message['content'] is sequence -%}\n {%- for item in message['content'] -%}\n {%- if item['type'] == 'text' -%}\n {%- if role == 'model' -%}\n {{- strip_thinking(item['text']) -}}\n {%- else -%}\n {{- item['text'] | trim -}}\n {%- endif -%}\n {%- elif item['type'] == 'image' -%}\n {{- '<|image|>' -}}\n {%- set ns.prev_message_type = 'image' -%}\n {%- elif item['type'] == 'audio' -%}\n {{- '<|audio|>' -}}\n {%- set ns.prev_message_type = 'audio' -%}\n {%- elif item['type'] == 'video' -%}\n {{- '<|video|>' -}}\n {%- set ns.prev_message_type = 'video' -%}\n {%- endif -%}\n {%- endfor -%}\n {%- endif -%}\n\n {%- if ns.prev_message_type == 'tool_call' and not ns_tr_out.flag -%}\n {{- '<|tool_response>' -}}\n {%- elif not (ns_tr_out.flag and not message.get('content')) -%}\n {{- '<turn|>\\n' -}}\n {%- endif -%}\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 {%- endif -%}\n{%- endif -%}"
|
| 96 |
+
}
|