Instructions to use luizaaca/qwen3-1.7b-clinical-screening with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use luizaaca/qwen3-1.7b-clinical-screening with PEFT:
Task type is invalid.
- Transformers
How to use luizaaca/qwen3-1.7b-clinical-screening with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="luizaaca/qwen3-1.7b-clinical-screening") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("luizaaca/qwen3-1.7b-clinical-screening", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use luizaaca/qwen3-1.7b-clinical-screening 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 luizaaca/qwen3-1.7b-clinical-screening:Q4_K_M # Run inference directly in the terminal: llama cli -hf luizaaca/qwen3-1.7b-clinical-screening:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf luizaaca/qwen3-1.7b-clinical-screening:Q4_K_M # Run inference directly in the terminal: llama cli -hf luizaaca/qwen3-1.7b-clinical-screening: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 luizaaca/qwen3-1.7b-clinical-screening:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf luizaaca/qwen3-1.7b-clinical-screening: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 luizaaca/qwen3-1.7b-clinical-screening:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf luizaaca/qwen3-1.7b-clinical-screening:Q4_K_M
Use Docker
docker model run hf.co/luizaaca/qwen3-1.7b-clinical-screening:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use luizaaca/qwen3-1.7b-clinical-screening with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "luizaaca/qwen3-1.7b-clinical-screening" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "luizaaca/qwen3-1.7b-clinical-screening", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/luizaaca/qwen3-1.7b-clinical-screening:Q4_K_M
- SGLang
How to use luizaaca/qwen3-1.7b-clinical-screening 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 "luizaaca/qwen3-1.7b-clinical-screening" \ --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": "luizaaca/qwen3-1.7b-clinical-screening", "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 "luizaaca/qwen3-1.7b-clinical-screening" \ --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": "luizaaca/qwen3-1.7b-clinical-screening", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use luizaaca/qwen3-1.7b-clinical-screening with Ollama:
ollama run hf.co/luizaaca/qwen3-1.7b-clinical-screening:Q4_K_M
- Unsloth Studio
How to use luizaaca/qwen3-1.7b-clinical-screening 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 luizaaca/qwen3-1.7b-clinical-screening 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 luizaaca/qwen3-1.7b-clinical-screening to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for luizaaca/qwen3-1.7b-clinical-screening to start chatting
- Pi
How to use luizaaca/qwen3-1.7b-clinical-screening with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf luizaaca/qwen3-1.7b-clinical-screening: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": "luizaaca/qwen3-1.7b-clinical-screening:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use luizaaca/qwen3-1.7b-clinical-screening with Docker Model Runner:
docker model run hf.co/luizaaca/qwen3-1.7b-clinical-screening:Q4_K_M
- Lemonade
How to use luizaaca/qwen3-1.7b-clinical-screening with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull luizaaca/qwen3-1.7b-clinical-screening:Q4_K_M
Run and chat with the model
lemonade run user.qwen3-1.7b-clinical-screening-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use luizaaca/qwen3-1.7b-clinical-screening with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf luizaaca/qwen3-1.7b-clinical-screening: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 luizaaca/qwen3-1.7b-clinical-screening:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use luizaaca/qwen3-1.7b-clinical-screening with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf luizaaca/qwen3-1.7b-clinical-screening: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 "luizaaca/qwen3-1.7b-clinical-screening: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 Qwen3 1.7B clinical screening adapter, GGUF artifact, model card, and Modelfile
Browse files- .gitattributes +3 -0
- Modelfile +67 -0
- README.md +113 -0
- gguf/chat_template.jinja +85 -0
- gguf/config.json +64 -0
- gguf/qwen3-1.7b-clinical-screening.Q4_K_M.gguf +3 -0
- gguf/tokenizer.json +3 -0
- gguf/tokenizer_config.json +226 -0
- lora/adapter_config.json +52 -0
- lora/adapter_model.safetensors +3 -0
- lora/chat_template.jinja +85 -0
- lora/tokenizer.json +3 -0
- lora/tokenizer_config.json +15 -0
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*.zip filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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gguf/qwen3-1.7b-clinical-screening.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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gguf/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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lora/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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FROM ./gguf/qwen3-1.7b-clinical-screening.Q4_K_M.gguf
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TEMPLATE """{{- if .Messages }}
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{{- if or .System .Tools }}<|im_start|>system
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{{- if .System }}
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{{ .System }}
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{{- end }}
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{{- if .Tools }}
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# Tools
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You may call one or more functions to assist with the user query.
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You are provided with function signatures within <tools></tools> XML tags:
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<tools>
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{{- range .Tools }}
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{"type": "function", "function": {{ .Function }}}
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{{- end }}
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</tools>
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For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
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<tool_call>
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{"name": <function-name>, "arguments": <args-json-object>}
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</tool_call>
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{{- end }}<|im_end|>
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{{ end }}
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{{- range $i, $_ := .Messages }}
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{{- $last := eq (len (slice $.Messages $i)) 1 -}}
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{{- if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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{{ else if eq .Role "assistant" }}<|im_start|>assistant
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{{ if .Content }}{{ .Content }}
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{{- else if .ToolCalls }}<tool_call>
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{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
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{{ end }}</tool_call>
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{{- end }}{{ if not $last }}<|im_end|>
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{{ end }}
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{{- else if eq .Role "tool" }}<|im_start|>user
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<tool_response>
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{{ .Content }}
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</tool_response><|im_end|>
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{{ end }}
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{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
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{{ end }}
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{{- end }}
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{{- else }}
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{{- if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ if .Prompt }}<|im_start|>user
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{{ .Prompt }}<|im_end|>
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{{ end }}<|im_start|>assistant
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{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
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PARAMETER stop "<|im_end|>"
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PARAMETER stop "<|im_start|>"
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PARAMETER temperature 0
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PARAMETER repeat_penalty 1.1
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PARAMETER num_ctx 2048
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SYSTEM """You are a clinical screening assistant focused on symptom analysis.
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Your job is to:
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- interpret the user's complaint in context;
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- recommend relevant confirmatory exams or next assessment steps;
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Output requirements:
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- Respond in the same language as the user, eg., English, Spanish, French.
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- Produce a structured internal result in JSON format.
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- Return only the JSON object.
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- Use exactly these keys in the JSON object: support_status, candidate_diseases, recommended_exams_tests."""
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---
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license: cc-by-4.0
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---
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---
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license: cc-by-4.0
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language:
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- en
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library_name: peft
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pipeline_tag: text-generation
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base_model: Qwen/Qwen3-1.7B-Base
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base_model_relation: finetune
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datasets:
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- luizaaca/symptoms-to-diseases-with-reasoning
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tags:
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- medical
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- clinical-screening
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- symptom-analysis
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- structured-output
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- json
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- lora
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- qlora
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- gguf
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- llama.cpp
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- ollama
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- unsloth
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- peft
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- transformers
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---
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# Qwen3-1.7B Clinical Screening
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This repository packages the artifacts generated by the training notebook
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`screening_robot_qwen3_1_7b_json.ipynb` for the `symptom_analysis`
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specialist step of the Screening Robot workflow.
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## Artifact layout
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- `lora/`: PEFT LoRA adapter weights and tokenizer/chat-template files.
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- `gguf/qwen3-1.7b-clinical-screening.Q4_K_M.gguf`: merged and quantized GGUF export (`Q4_K_M`) for
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llama.cpp and Ollama-style runtimes.
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- `gguf/config.json`, `gguf/tokenizer.json`, `gguf/tokenizer_config.json`,
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`gguf/chat_template.jinja`: support files exported alongside the GGUF build.
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- `Modelfile`: Ollama-oriented template aligned with the same JSON-focused
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system prompt used in the training notebook.
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## Model details
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- **Repository**: `https://huggingface.co/luizaaca/qwen3-1.7b-clinical-screening`
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- **Base model**: `Qwen/Qwen3-1.7B-Base`
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- **Training runtime base**: `unsloth/qwen3-1.7b-base-unsloth-bnb-4bit`
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- **Training recipe**: QLoRA via Unsloth on a 4-bit loaded base model
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- **LoRA hyperparameters**: rank 16, alpha 32, dropout 0.0
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- **Target modules**: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`,
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`up_proj`, `down_proj`
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- **Max sequence length used during training**: 1024
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- **Training runtime**: Google Colab + Tesla T4 (16 GB VRAM)
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- **Dataset**: `luizaaca/symptoms-to-diseases-with-reasoning`
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## Intended behavior
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The model was fine-tuned to emit a compact JSON object with exactly three keys:
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- `support_status`
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- `candidate_diseases`
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- `recommended_exams_tests`
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| 63 |
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The fine-tuning notebook explicitly scoped the model to the
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dataset-aligned specialist contract used by the `symptom_analysis` node.
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It does **not** represent the full downstream production payload used
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elsewhere in the application.
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## Prompt contract
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| 70 |
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Training and validation were built around the following behavior:
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| 72 |
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| 73 |
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- a clinical screening system prompt focused on symptom analysis;
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- a user message formatted as `Clinical request` plus `Active patient context`; and
|
| 75 |
+
- an assistant answer restricted to JSON only.
|
| 76 |
+
|
| 77 |
+
The default `Modelfile` included in this repository mirrors that setup.
|
| 78 |
+
|
| 79 |
+
## Validation summary
|
| 80 |
+
|
| 81 |
+
The notebook includes:
|
| 82 |
+
|
| 83 |
+
- a held-out evaluation pass comparing the fine-tuned model against the base model;
|
| 84 |
+
- an assertion that fine-tuned label accuracy matches or exceeds the base model on the evaluation split; and
|
| 85 |
+
- GGUF smoke tests that require the exported model to emit valid schema-compliant JSON.
|
| 86 |
+
|
| 87 |
+
No claim of clinical validation, diagnostic safety, or regulatory readiness is made.
|
| 88 |
+
|
| 89 |
+
## Notes about the GGUF filename
|
| 90 |
+
|
| 91 |
+
The local GGUF export produced by the toolchain may inherit the original base
|
| 92 |
+
model filename. In this repository the uploaded GGUF weight is renamed to
|
| 93 |
+
`qwen3-1.7b-clinical-screening.Q4_K_M.gguf` to make it explicit that the file contains the
|
| 94 |
+
fine-tuned clinical screening variant.
|
| 95 |
+
|
| 96 |
+
## Intended use
|
| 97 |
+
|
| 98 |
+
This repository is suitable for:
|
| 99 |
+
|
| 100 |
+
- research experiments on structured clinical-screening assistants;
|
| 101 |
+
- integration prototypes for symptom-intake workflows; and
|
| 102 |
+
- local inference with PEFT or GGUF-compatible runtimes.
|
| 103 |
+
|
| 104 |
+
## Out-of-scope use
|
| 105 |
+
|
| 106 |
+
This repository is **not** intended for:
|
| 107 |
+
|
| 108 |
+
- autonomous diagnosis or treatment decisions;
|
| 109 |
+
- emergency or high-acuity triage without clinician oversight;
|
| 110 |
+
- prescribing or medication guidance; or
|
| 111 |
+
- use as a substitute for professional medical judgment.
|
| 112 |
+
|
| 113 |
+
## Safety notice
|
| 114 |
+
|
| 115 |
+
This is a research artifact for screening-assistance workflows only. Always
|
| 116 |
+
keep a qualified human clinician in the loop.
|
|
@@ -0,0 +1,85 @@
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|
| 1 |
+
{%- if tools %}
|
| 2 |
+
{{- '<|im_start|>system\n' }}
|
| 3 |
+
{%- if messages[0].role == 'system' %}
|
| 4 |
+
{{- messages[0].content + '\n\n' }}
|
| 5 |
+
{%- endif %}
|
| 6 |
+
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
| 7 |
+
{%- for tool in tools %}
|
| 8 |
+
{{- "\n" }}
|
| 9 |
+
{{- tool | tojson }}
|
| 10 |
+
{%- endfor %}
|
| 11 |
+
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
| 12 |
+
{%- else %}
|
| 13 |
+
{%- if messages[0].role == 'system' %}
|
| 14 |
+
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
| 15 |
+
{%- endif %}
|
| 16 |
+
{%- endif %}
|
| 17 |
+
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
| 18 |
+
{%- for message in messages[::-1] %}
|
| 19 |
+
{%- set index = (messages|length - 1) - loop.index0 %}
|
| 20 |
+
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
| 21 |
+
{%- set ns.multi_step_tool = false %}
|
| 22 |
+
{%- set ns.last_query_index = index %}
|
| 23 |
+
{%- endif %}
|
| 24 |
+
{%- endfor %}
|
| 25 |
+
{%- for message in messages %}
|
| 26 |
+
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
| 27 |
+
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
| 28 |
+
{%- elif message.role == "assistant" %}
|
| 29 |
+
{%- set content = message.content %}
|
| 30 |
+
{%- set reasoning_content = '' %}
|
| 31 |
+
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
|
| 32 |
+
{%- set reasoning_content = message.reasoning_content %}
|
| 33 |
+
{%- else %}
|
| 34 |
+
{%- if '</think>' in message.content %}
|
| 35 |
+
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
|
| 36 |
+
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
| 37 |
+
{%- endif %}
|
| 38 |
+
{%- endif %}
|
| 39 |
+
{%- if loop.index0 > ns.last_query_index %}
|
| 40 |
+
{%- if loop.last or (not loop.last and reasoning_content) %}
|
| 41 |
+
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
| 42 |
+
{%- else %}
|
| 43 |
+
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 44 |
+
{%- endif %}
|
| 45 |
+
{%- else %}
|
| 46 |
+
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 47 |
+
{%- endif %}
|
| 48 |
+
{%- if message.tool_calls %}
|
| 49 |
+
{%- for tool_call in message.tool_calls %}
|
| 50 |
+
{%- if (loop.first and content) or (not loop.first) %}
|
| 51 |
+
{{- '\n' }}
|
| 52 |
+
{%- endif %}
|
| 53 |
+
{%- if tool_call.function %}
|
| 54 |
+
{%- set tool_call = tool_call.function %}
|
| 55 |
+
{%- endif %}
|
| 56 |
+
{{- '<tool_call>\n{"name": "' }}
|
| 57 |
+
{{- tool_call.name }}
|
| 58 |
+
{{- '", "arguments": ' }}
|
| 59 |
+
{%- if tool_call.arguments is string %}
|
| 60 |
+
{{- tool_call.arguments }}
|
| 61 |
+
{%- else %}
|
| 62 |
+
{{- tool_call.arguments | tojson }}
|
| 63 |
+
{%- endif %}
|
| 64 |
+
{{- '}\n</tool_call>' }}
|
| 65 |
+
{%- endfor %}
|
| 66 |
+
{%- endif %}
|
| 67 |
+
{{- '<|im_end|>\n' }}
|
| 68 |
+
{%- elif message.role == "tool" %}
|
| 69 |
+
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
| 70 |
+
{{- '<|im_start|>user' }}
|
| 71 |
+
{%- endif %}
|
| 72 |
+
{{- '\n<tool_response>\n' }}
|
| 73 |
+
{{- message.content }}
|
| 74 |
+
{{- '\n</tool_response>' }}
|
| 75 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 76 |
+
{{- '<|im_end|>\n' }}
|
| 77 |
+
{%- endif %}
|
| 78 |
+
{%- endif %}
|
| 79 |
+
{%- endfor %}
|
| 80 |
+
{%- if add_generation_prompt %}
|
| 81 |
+
{{- '<|im_start|>assistant\n' }}
|
| 82 |
+
{%- if enable_thinking is defined and enable_thinking is false %}
|
| 83 |
+
{{- '<think>\n\n</think>\n\n' }}
|
| 84 |
+
{%- endif %}
|
| 85 |
+
{%- endif %}
|
|
@@ -0,0 +1,64 @@
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|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Qwen3ForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": null,
|
| 8 |
+
"torch_dtype": "float16",
|
| 9 |
+
"eos_token_id": 151645,
|
| 10 |
+
"head_dim": 128,
|
| 11 |
+
"hidden_act": "silu",
|
| 12 |
+
"hidden_size": 2048,
|
| 13 |
+
"initializer_range": 0.02,
|
| 14 |
+
"intermediate_size": 6144,
|
| 15 |
+
"layer_types": [
|
| 16 |
+
"full_attention",
|
| 17 |
+
"full_attention",
|
| 18 |
+
"full_attention",
|
| 19 |
+
"full_attention",
|
| 20 |
+
"full_attention",
|
| 21 |
+
"full_attention",
|
| 22 |
+
"full_attention",
|
| 23 |
+
"full_attention",
|
| 24 |
+
"full_attention",
|
| 25 |
+
"full_attention",
|
| 26 |
+
"full_attention",
|
| 27 |
+
"full_attention",
|
| 28 |
+
"full_attention",
|
| 29 |
+
"full_attention",
|
| 30 |
+
"full_attention",
|
| 31 |
+
"full_attention",
|
| 32 |
+
"full_attention",
|
| 33 |
+
"full_attention",
|
| 34 |
+
"full_attention",
|
| 35 |
+
"full_attention",
|
| 36 |
+
"full_attention",
|
| 37 |
+
"full_attention",
|
| 38 |
+
"full_attention",
|
| 39 |
+
"full_attention",
|
| 40 |
+
"full_attention",
|
| 41 |
+
"full_attention",
|
| 42 |
+
"full_attention",
|
| 43 |
+
"full_attention"
|
| 44 |
+
],
|
| 45 |
+
"max_position_embeddings": 32768,
|
| 46 |
+
"max_window_layers": 28,
|
| 47 |
+
"model_type": "qwen3",
|
| 48 |
+
"num_attention_heads": 16,
|
| 49 |
+
"num_hidden_layers": 28,
|
| 50 |
+
"num_key_value_heads": 8,
|
| 51 |
+
"pad_token_id": 151643,
|
| 52 |
+
"rms_norm_eps": 1e-06,
|
| 53 |
+
"rope_parameters": {
|
| 54 |
+
"rope_theta": 1000000,
|
| 55 |
+
"rope_type": "default"
|
| 56 |
+
},
|
| 57 |
+
"sliding_window": null,
|
| 58 |
+
"tie_word_embeddings": true,
|
| 59 |
+
"unsloth_fixed": true,
|
| 60 |
+
"unsloth_version": "2026.5.2",
|
| 61 |
+
"use_cache": false,
|
| 62 |
+
"use_sliding_window": false,
|
| 63 |
+
"vocab_size": 151936
|
| 64 |
+
}
|
|
@@ -0,0 +1,3 @@
|
|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f0dbb76e2bd0c726601f6600ee09f75bfbadea529ebd1f98643eaffa836ae83b
|
| 3 |
+
size 1107408672
|
|
@@ -0,0 +1,3 @@
|
|
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|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
|
| 3 |
+
size 11422650
|
|
@@ -0,0 +1,226 @@
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|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"backend": "tokenizers",
|
| 4 |
+
"bos_token": null,
|
| 5 |
+
"clean_up_tokenization_spaces": false,
|
| 6 |
+
"eos_token": "<|im_end|>",
|
| 7 |
+
"errors": "replace",
|
| 8 |
+
"extra_special_tokens": [],
|
| 9 |
+
"is_local": false,
|
| 10 |
+
"model_max_length": 131072,
|
| 11 |
+
"pad_token": "<|endoftext|>",
|
| 12 |
+
"split_special_tokens": false,
|
| 13 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 14 |
+
"unk_token": null,
|
| 15 |
+
"added_tokens_decoder": {
|
| 16 |
+
"151643": {
|
| 17 |
+
"content": "<|endoftext|>",
|
| 18 |
+
"single_word": false,
|
| 19 |
+
"lstrip": false,
|
| 20 |
+
"rstrip": false,
|
| 21 |
+
"normalized": false,
|
| 22 |
+
"special": true
|
| 23 |
+
},
|
| 24 |
+
"151644": {
|
| 25 |
+
"content": "<|im_start|>",
|
| 26 |
+
"single_word": false,
|
| 27 |
+
"lstrip": false,
|
| 28 |
+
"rstrip": false,
|
| 29 |
+
"normalized": false,
|
| 30 |
+
"special": true
|
| 31 |
+
},
|
| 32 |
+
"151645": {
|
| 33 |
+
"content": "<|im_end|>",
|
| 34 |
+
"single_word": false,
|
| 35 |
+
"lstrip": false,
|
| 36 |
+
"rstrip": false,
|
| 37 |
+
"normalized": false,
|
| 38 |
+
"special": true
|
| 39 |
+
},
|
| 40 |
+
"151646": {
|
| 41 |
+
"content": "<|object_ref_start|>",
|
| 42 |
+
"single_word": false,
|
| 43 |
+
"lstrip": false,
|
| 44 |
+
"rstrip": false,
|
| 45 |
+
"normalized": false,
|
| 46 |
+
"special": true
|
| 47 |
+
},
|
| 48 |
+
"151647": {
|
| 49 |
+
"content": "<|object_ref_end|>",
|
| 50 |
+
"single_word": false,
|
| 51 |
+
"lstrip": false,
|
| 52 |
+
"rstrip": false,
|
| 53 |
+
"normalized": false,
|
| 54 |
+
"special": true
|
| 55 |
+
},
|
| 56 |
+
"151648": {
|
| 57 |
+
"content": "<|box_start|>",
|
| 58 |
+
"single_word": false,
|
| 59 |
+
"lstrip": false,
|
| 60 |
+
"rstrip": false,
|
| 61 |
+
"normalized": false,
|
| 62 |
+
"special": true
|
| 63 |
+
},
|
| 64 |
+
"151649": {
|
| 65 |
+
"content": "<|box_end|>",
|
| 66 |
+
"single_word": false,
|
| 67 |
+
"lstrip": false,
|
| 68 |
+
"rstrip": false,
|
| 69 |
+
"normalized": false,
|
| 70 |
+
"special": true
|
| 71 |
+
},
|
| 72 |
+
"151650": {
|
| 73 |
+
"content": "<|quad_start|>",
|
| 74 |
+
"single_word": false,
|
| 75 |
+
"lstrip": false,
|
| 76 |
+
"rstrip": false,
|
| 77 |
+
"normalized": false,
|
| 78 |
+
"special": true
|
| 79 |
+
},
|
| 80 |
+
"151651": {
|
| 81 |
+
"content": "<|quad_end|>",
|
| 82 |
+
"single_word": false,
|
| 83 |
+
"lstrip": false,
|
| 84 |
+
"rstrip": false,
|
| 85 |
+
"normalized": false,
|
| 86 |
+
"special": true
|
| 87 |
+
},
|
| 88 |
+
"151652": {
|
| 89 |
+
"content": "<|vision_start|>",
|
| 90 |
+
"single_word": false,
|
| 91 |
+
"lstrip": false,
|
| 92 |
+
"rstrip": false,
|
| 93 |
+
"normalized": false,
|
| 94 |
+
"special": true
|
| 95 |
+
},
|
| 96 |
+
"151653": {
|
| 97 |
+
"content": "<|vision_end|>",
|
| 98 |
+
"single_word": false,
|
| 99 |
+
"lstrip": false,
|
| 100 |
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"rstrip": false,
|
| 101 |
+
"normalized": false,
|
| 102 |
+
"special": true
|
| 103 |
+
},
|
| 104 |
+
"151654": {
|
| 105 |
+
"content": "<|vision_pad|>",
|
| 106 |
+
"single_word": false,
|
| 107 |
+
"lstrip": false,
|
| 108 |
+
"rstrip": false,
|
| 109 |
+
"normalized": false,
|
| 110 |
+
"special": true
|
| 111 |
+
},
|
| 112 |
+
"151655": {
|
| 113 |
+
"content": "<|image_pad|>",
|
| 114 |
+
"single_word": false,
|
| 115 |
+
"lstrip": false,
|
| 116 |
+
"rstrip": false,
|
| 117 |
+
"normalized": false,
|
| 118 |
+
"special": true
|
| 119 |
+
},
|
| 120 |
+
"151656": {
|
| 121 |
+
"content": "<|video_pad|>",
|
| 122 |
+
"single_word": false,
|
| 123 |
+
"lstrip": false,
|
| 124 |
+
"rstrip": false,
|
| 125 |
+
"normalized": false,
|
| 126 |
+
"special": true
|
| 127 |
+
},
|
| 128 |
+
"151657": {
|
| 129 |
+
"content": "<tool_call>",
|
| 130 |
+
"single_word": false,
|
| 131 |
+
"lstrip": false,
|
| 132 |
+
"rstrip": false,
|
| 133 |
+
"normalized": false,
|
| 134 |
+
"special": false
|
| 135 |
+
},
|
| 136 |
+
"151658": {
|
| 137 |
+
"content": "</tool_call>",
|
| 138 |
+
"single_word": false,
|
| 139 |
+
"lstrip": false,
|
| 140 |
+
"rstrip": false,
|
| 141 |
+
"normalized": false,
|
| 142 |
+
"special": false
|
| 143 |
+
},
|
| 144 |
+
"151659": {
|
| 145 |
+
"content": "<|fim_prefix|>",
|
| 146 |
+
"single_word": false,
|
| 147 |
+
"lstrip": false,
|
| 148 |
+
"rstrip": false,
|
| 149 |
+
"normalized": false,
|
| 150 |
+
"special": false
|
| 151 |
+
},
|
| 152 |
+
"151660": {
|
| 153 |
+
"content": "<|fim_middle|>",
|
| 154 |
+
"single_word": false,
|
| 155 |
+
"lstrip": false,
|
| 156 |
+
"rstrip": false,
|
| 157 |
+
"normalized": false,
|
| 158 |
+
"special": false
|
| 159 |
+
},
|
| 160 |
+
"151661": {
|
| 161 |
+
"content": "<|fim_suffix|>",
|
| 162 |
+
"single_word": false,
|
| 163 |
+
"lstrip": false,
|
| 164 |
+
"rstrip": false,
|
| 165 |
+
"normalized": false,
|
| 166 |
+
"special": false
|
| 167 |
+
},
|
| 168 |
+
"151662": {
|
| 169 |
+
"content": "<|fim_pad|>",
|
| 170 |
+
"single_word": false,
|
| 171 |
+
"lstrip": false,
|
| 172 |
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"rstrip": false,
|
| 173 |
+
"normalized": false,
|
| 174 |
+
"special": false
|
| 175 |
+
},
|
| 176 |
+
"151663": {
|
| 177 |
+
"content": "<|repo_name|>",
|
| 178 |
+
"single_word": false,
|
| 179 |
+
"lstrip": false,
|
| 180 |
+
"rstrip": false,
|
| 181 |
+
"normalized": false,
|
| 182 |
+
"special": false
|
| 183 |
+
},
|
| 184 |
+
"151664": {
|
| 185 |
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"content": "<|file_sep|>",
|
| 186 |
+
"single_word": false,
|
| 187 |
+
"lstrip": false,
|
| 188 |
+
"rstrip": false,
|
| 189 |
+
"normalized": false,
|
| 190 |
+
"special": false
|
| 191 |
+
},
|
| 192 |
+
"151665": {
|
| 193 |
+
"content": "<tool_response>",
|
| 194 |
+
"single_word": false,
|
| 195 |
+
"lstrip": false,
|
| 196 |
+
"rstrip": false,
|
| 197 |
+
"normalized": false,
|
| 198 |
+
"special": false
|
| 199 |
+
},
|
| 200 |
+
"151666": {
|
| 201 |
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"content": "</tool_response>",
|
| 202 |
+
"single_word": false,
|
| 203 |
+
"lstrip": false,
|
| 204 |
+
"rstrip": false,
|
| 205 |
+
"normalized": false,
|
| 206 |
+
"special": false
|
| 207 |
+
},
|
| 208 |
+
"151667": {
|
| 209 |
+
"content": "<think>",
|
| 210 |
+
"single_word": false,
|
| 211 |
+
"lstrip": false,
|
| 212 |
+
"rstrip": false,
|
| 213 |
+
"normalized": false,
|
| 214 |
+
"special": false
|
| 215 |
+
},
|
| 216 |
+
"151668": {
|
| 217 |
+
"content": "</think>",
|
| 218 |
+
"single_word": false,
|
| 219 |
+
"lstrip": false,
|
| 220 |
+
"rstrip": false,
|
| 221 |
+
"normalized": false,
|
| 222 |
+
"special": false
|
| 223 |
+
}
|
| 224 |
+
},
|
| 225 |
+
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}"
|
| 226 |
+
}
|
|
@@ -0,0 +1,52 @@
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|
| 1 |
+
{
|
| 2 |
+
"alora_invocation_tokens": null,
|
| 3 |
+
"alpha_pattern": {},
|
| 4 |
+
"arrow_config": null,
|
| 5 |
+
"auto_mapping": {
|
| 6 |
+
"base_model_class": "Qwen3ForCausalLM",
|
| 7 |
+
"parent_library": "transformers.models.qwen3.modeling_qwen3",
|
| 8 |
+
"unsloth_fixed": true
|
| 9 |
+
},
|
| 10 |
+
"base_model_name_or_path": "unsloth/qwen3-1.7b-base-unsloth-bnb-4bit",
|
| 11 |
+
"bias": "none",
|
| 12 |
+
"corda_config": null,
|
| 13 |
+
"ensure_weight_tying": false,
|
| 14 |
+
"eva_config": null,
|
| 15 |
+
"exclude_modules": null,
|
| 16 |
+
"fan_in_fan_out": false,
|
| 17 |
+
"inference_mode": true,
|
| 18 |
+
"init_lora_weights": true,
|
| 19 |
+
"layer_replication": null,
|
| 20 |
+
"layers_pattern": null,
|
| 21 |
+
"layers_to_transform": null,
|
| 22 |
+
"loftq_config": {},
|
| 23 |
+
"lora_alpha": 32,
|
| 24 |
+
"lora_bias": false,
|
| 25 |
+
"lora_dropout": 0.0,
|
| 26 |
+
"lora_ga_config": null,
|
| 27 |
+
"megatron_config": null,
|
| 28 |
+
"megatron_core": "megatron.core",
|
| 29 |
+
"modules_to_save": null,
|
| 30 |
+
"peft_type": "LORA",
|
| 31 |
+
"peft_version": "0.19.1",
|
| 32 |
+
"qalora_group_size": 16,
|
| 33 |
+
"r": 16,
|
| 34 |
+
"rank_pattern": {},
|
| 35 |
+
"revision": null,
|
| 36 |
+
"target_modules": [
|
| 37 |
+
"v_proj",
|
| 38 |
+
"q_proj",
|
| 39 |
+
"o_proj",
|
| 40 |
+
"up_proj",
|
| 41 |
+
"k_proj",
|
| 42 |
+
"down_proj",
|
| 43 |
+
"gate_proj"
|
| 44 |
+
],
|
| 45 |
+
"target_parameters": null,
|
| 46 |
+
"task_type": "CAUSAL_LM",
|
| 47 |
+
"trainable_token_indices": null,
|
| 48 |
+
"use_bdlora": null,
|
| 49 |
+
"use_dora": false,
|
| 50 |
+
"use_qalora": false,
|
| 51 |
+
"use_rslora": false
|
| 52 |
+
}
|
|
@@ -0,0 +1,3 @@
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+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2f9e5252209c268a85db46791210a6863a9ec7e03130e34fc53d99fc70f3adba
|
| 3 |
+
size 69782384
|
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@@ -0,0 +1,85 @@
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|
| 1 |
+
{%- if tools %}
|
| 2 |
+
{{- '<|im_start|>system\n' }}
|
| 3 |
+
{%- if messages[0].role == 'system' %}
|
| 4 |
+
{{- messages[0].content + '\n\n' }}
|
| 5 |
+
{%- endif %}
|
| 6 |
+
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
| 7 |
+
{%- for tool in tools %}
|
| 8 |
+
{{- "\n" }}
|
| 9 |
+
{{- tool | tojson }}
|
| 10 |
+
{%- endfor %}
|
| 11 |
+
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
| 12 |
+
{%- else %}
|
| 13 |
+
{%- if messages[0].role == 'system' %}
|
| 14 |
+
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
| 15 |
+
{%- endif %}
|
| 16 |
+
{%- endif %}
|
| 17 |
+
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
| 18 |
+
{%- for message in messages[::-1] %}
|
| 19 |
+
{%- set index = (messages|length - 1) - loop.index0 %}
|
| 20 |
+
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
| 21 |
+
{%- set ns.multi_step_tool = false %}
|
| 22 |
+
{%- set ns.last_query_index = index %}
|
| 23 |
+
{%- endif %}
|
| 24 |
+
{%- endfor %}
|
| 25 |
+
{%- for message in messages %}
|
| 26 |
+
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
| 27 |
+
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
| 28 |
+
{%- elif message.role == "assistant" %}
|
| 29 |
+
{%- set content = message.content %}
|
| 30 |
+
{%- set reasoning_content = '' %}
|
| 31 |
+
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
|
| 32 |
+
{%- set reasoning_content = message.reasoning_content %}
|
| 33 |
+
{%- else %}
|
| 34 |
+
{%- if '</think>' in message.content %}
|
| 35 |
+
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
|
| 36 |
+
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
| 37 |
+
{%- endif %}
|
| 38 |
+
{%- endif %}
|
| 39 |
+
{%- if loop.index0 > ns.last_query_index %}
|
| 40 |
+
{%- if loop.last or (not loop.last and reasoning_content) %}
|
| 41 |
+
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
| 42 |
+
{%- else %}
|
| 43 |
+
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 44 |
+
{%- endif %}
|
| 45 |
+
{%- else %}
|
| 46 |
+
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 47 |
+
{%- endif %}
|
| 48 |
+
{%- if message.tool_calls %}
|
| 49 |
+
{%- for tool_call in message.tool_calls %}
|
| 50 |
+
{%- if (loop.first and content) or (not loop.first) %}
|
| 51 |
+
{{- '\n' }}
|
| 52 |
+
{%- endif %}
|
| 53 |
+
{%- if tool_call.function %}
|
| 54 |
+
{%- set tool_call = tool_call.function %}
|
| 55 |
+
{%- endif %}
|
| 56 |
+
{{- '<tool_call>\n{"name": "' }}
|
| 57 |
+
{{- tool_call.name }}
|
| 58 |
+
{{- '", "arguments": ' }}
|
| 59 |
+
{%- if tool_call.arguments is string %}
|
| 60 |
+
{{- tool_call.arguments }}
|
| 61 |
+
{%- else %}
|
| 62 |
+
{{- tool_call.arguments | tojson }}
|
| 63 |
+
{%- endif %}
|
| 64 |
+
{{- '}\n</tool_call>' }}
|
| 65 |
+
{%- endfor %}
|
| 66 |
+
{%- endif %}
|
| 67 |
+
{{- '<|im_end|>\n' }}
|
| 68 |
+
{%- elif message.role == "tool" %}
|
| 69 |
+
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
| 70 |
+
{{- '<|im_start|>user' }}
|
| 71 |
+
{%- endif %}
|
| 72 |
+
{{- '\n<tool_response>\n' }}
|
| 73 |
+
{{- message.content }}
|
| 74 |
+
{{- '\n</tool_response>' }}
|
| 75 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 76 |
+
{{- '<|im_end|>\n' }}
|
| 77 |
+
{%- endif %}
|
| 78 |
+
{%- endif %}
|
| 79 |
+
{%- endfor %}
|
| 80 |
+
{%- if add_generation_prompt %}
|
| 81 |
+
{{- '<|im_start|>assistant\n' }}
|
| 82 |
+
{%- if enable_thinking is defined and enable_thinking is false %}
|
| 83 |
+
{{- '<think>\n\n</think>\n\n' }}
|
| 84 |
+
{%- endif %}
|
| 85 |
+
{%- endif %}
|
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e946ac23b6a68f7a2abbe7b3c22190673c6d3d159b85305268db51b2729ac68a
|
| 3 |
+
size 11422749
|
|
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"backend": "tokenizers",
|
| 4 |
+
"bos_token": null,
|
| 5 |
+
"clean_up_tokenization_spaces": false,
|
| 6 |
+
"eos_token": "<|im_end|>",
|
| 7 |
+
"errors": "replace",
|
| 8 |
+
"extra_special_tokens": [],
|
| 9 |
+
"is_local": false,
|
| 10 |
+
"model_max_length": 131072,
|
| 11 |
+
"pad_token": "<|endoftext|>",
|
| 12 |
+
"split_special_tokens": false,
|
| 13 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 14 |
+
"unk_token": null
|
| 15 |
+
}
|