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.
```

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

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"

qwen3-1.7b-clinical-screening / Modelfile

luizaaca

Upload Qwen3 1.7B clinical screening adapter, GGUF artifact, model card, and Modelfile

4b8d890 verified 3 months ago

Raw

History Blame Contribute Delete

2.25 kB

	FROM ./gguf/qwen3-1.7b-clinical-screening.Q4_K_M.gguf
	TEMPLATE """{{- if .Messages }}
	{{- if or .System .Tools }}<\|im_start\|>system
	{{- if .System }}
	{{ .System }}
	{{- end }}
	{{- if .Tools }}

	# Tools

	You may call one or more functions to assist with the user query.

	You are provided with function signatures within <tools></tools> XML tags:
	<tools>
	{{- range .Tools }}
	{"type": "function", "function": {{ .Function }}}
	{{- end }}
	</tools>

	For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
	<tool_call>
	{"name": <function-name>, "arguments": <args-json-object>}
	</tool_call>
	{{- end }}<\|im_end\|>
	{{ end }}
	{{- range $i, $_ := .Messages }}
	{{- $last := eq (len (slice $.Messages $i)) 1 -}}
	{{- if eq .Role "user" }}<\|im_start\|>user
	{{ .Content }}<\|im_end\|>
	{{ else if eq .Role "assistant" }}<\|im_start\|>assistant
	{{ if .Content }}{{ .Content }}
	{{- else if .ToolCalls }}<tool_call>
	{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
	{{ end }}</tool_call>
	{{- end }}{{ if not $last }}<\|im_end\|>
	{{ end }}
	{{- else if eq .Role "tool" }}<\|im_start\|>user
	<tool_response>
	{{ .Content }}
	</tool_response><\|im_end\|>
	{{ end }}
	{{- if and (ne .Role "assistant") $last }}<\|im_start\|>assistant
	{{ end }}
	{{- end }}
	{{- else }}
	{{- if .System }}<\|im_start\|>system
	{{ .System }}<\|im_end\|>
	{{ end }}{{ if .Prompt }}<\|im_start\|>user
	{{ .Prompt }}<\|im_end\|>
	{{ end }}<\|im_start\|>assistant
	{{ end }}{{ .Response }}{{ if .Response }}<\|im_end\|>{{ end }}"""
	PARAMETER stop "<\|im_end\|>"
	PARAMETER stop "<\|im_start\|>"
	PARAMETER temperature 0
	PARAMETER repeat_penalty 1.1
	PARAMETER num_ctx 2048
	SYSTEM """You are a clinical screening assistant focused on symptom analysis.

	Your job is to:
	- interpret the user's complaint in context;
	- recommend relevant confirmatory exams or next assessment steps;

	Output requirements:
	- Respond in the same language as the user, eg., English, Spanish, French.
	- Produce a structured internal result in JSON format.
	- Return only the JSON object.
	- Use exactly these keys in the JSON object: support_status, candidate_diseases, recommended_exams_tests."""