Instructions to use luizaaca/qwen3-0.6b-clinical-screening with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use luizaaca/qwen3-0.6b-clinical-screening with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="luizaaca/qwen3-0.6b-clinical-screening")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("luizaaca/qwen3-0.6b-clinical-screening")
model = AutoModelForCausalLM.from_pretrained("luizaaca/qwen3-0.6b-clinical-screening", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use luizaaca/qwen3-0.6b-clinical-screening with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use luizaaca/qwen3-0.6b-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/qwen3-0.6b-clinical-screening:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf luizaaca/qwen3-0.6b-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/qwen3-0.6b-clinical-screening:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf luizaaca/qwen3-0.6b-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/qwen3-0.6b-clinical-screening:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf luizaaca/qwen3-0.6b-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/qwen3-0.6b-clinical-screening:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf luizaaca/qwen3-0.6b-clinical-screening:Q4_K_M

Use Docker

docker model run hf.co/luizaaca/qwen3-0.6b-clinical-screening:Q4_K_M

LM Studio
Jan

vLLM

How to use luizaaca/qwen3-0.6b-clinical-screening with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "luizaaca/qwen3-0.6b-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/qwen3-0.6b-clinical-screening",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/luizaaca/qwen3-0.6b-clinical-screening:Q4_K_M

SGLang

How to use luizaaca/qwen3-0.6b-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/qwen3-0.6b-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/qwen3-0.6b-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/qwen3-0.6b-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/qwen3-0.6b-clinical-screening",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use luizaaca/qwen3-0.6b-clinical-screening with Ollama:
```
ollama run hf.co/luizaaca/qwen3-0.6b-clinical-screening:Q4_K_M
```

Unsloth Studio

How to use luizaaca/qwen3-0.6b-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/qwen3-0.6b-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/qwen3-0.6b-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/qwen3-0.6b-clinical-screening to start chatting

How to use luizaaca/qwen3-0.6b-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/qwen3-0.6b-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/qwen3-0.6b-clinical-screening:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use luizaaca/qwen3-0.6b-clinical-screening with Docker Model Runner:
```
docker model run hf.co/luizaaca/qwen3-0.6b-clinical-screening:Q4_K_M
```

Lemonade

How to use luizaaca/qwen3-0.6b-clinical-screening with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull luizaaca/qwen3-0.6b-clinical-screening:Q4_K_M

Run and chat with the model

lemonade run user.qwen3-0.6b-clinical-screening-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use luizaaca/qwen3-0.6b-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/qwen3-0.6b-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/qwen3-0.6b-clinical-screening:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use luizaaca/qwen3-0.6b-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/qwen3-0.6b-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/qwen3-0.6b-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"

Qwen3-0.6B Clinical Screening

This repository packages the artifacts generated by the training notebook screening_robot.ipynb for a compact clinical screening assistant based on Qwen3-0.6B.

Artifact layout

Root-level merged model files (model.safetensors, config.json, tokenizer.json, tokenizer_config.json, chat_template.jinja) for direct transformers loading.
lora/: PEFT LoRA adapter weights and tokenizer/chat-template files.
gguf/qwen3-0.6b-clinical-screening.Q4_K_M.gguf: quantized GGUF export (Q4_K_M) for llama.cpp, Ollama, LM Studio, and similar runtimes.
Modelfile: Ollama-oriented prompt wrapper aligned with the training prompt and inference settings used in the notebook examples.

Model details

Repository: https://huggingface.co/luizaaca/qwen3-0.6b-clinical-screening
Base model: Qwen/Qwen3-0.6B
Training runtime base: unsloth/Qwen3-0.6B-unsloth-bnb-4bit
Training recipe: QLoRA via Unsloth on a 4-bit loaded base model
LoRA hyperparameters: rank 16, alpha 32, dropout 0.0
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Max sequence length used during training: 1024
Training steps: 400
Target hardware: Google Colab Free with Tesla T4 (16 GB VRAM)

Training data

The checked-in notebook trains on two Kaggle datasets:

dhivyeshrk/diseases-and-symptoms-dataset: binary symptom-matrix data converted to natural-language symptom lists, with a stratified cap of 50 examples per disease before the train/test split.
niyarrbarman/symptom2disease: free-text symptom descriptions mapped directly to disease labels.

Output contract

The assistant is fine-tuned to answer using the standardized disclaimer format:

Based on the reported symptoms, the clinical indication points to: <disease>.
Disclaimer: This is an AI auxiliary tool designed for healthcare professionals. It is not 100% precise and does not replace a professional medical diagnosis.

This is a plain-text disease-identification assistant. Unlike the 1.7B JSON specialist model, this 0.6B notebook does not train on a structured JSON schema.

Prompting notes

The training and inference flow uses a system prompt that frames the model as a clinical AI assistant and user prompts shaped like:

Given the symptoms reported, identify the disease.

Symptoms: ... /no_think

The notebook markdown discusses mixed thinking/no-thinking supervision, but the effective checked-in configuration sets thinking_ratio = 0, so the actual supervised examples follow the no-thinking path.

Validation summary

The notebook evaluates the base model against the fine-tuned model on held-out splits from both datasets and reports:

qualitative side-by-side generations;
Accuracy and macro-F1;
Cohen's Kappa;
row-normalized confusion matrices; and
BERTScore.

The code also asserts that fine-tuned accuracy on Dataset 2 matches or exceeds the base model.

Intended use

This repository is suitable for:

research experiments on lightweight clinical-screening assistants;
teaching and prototyping around symptom-to-disease prompting; and
local inference with Transformers, PEFT, GGUF-compatible runtimes, or Ollama.

Out-of-scope use

This repository is not intended for:

autonomous diagnosis or treatment decisions;
emergency triage without clinician oversight;
prescribing or medication guidance; or
use as a substitute for professional medical judgment.

Safety notice

This is a research artifact for healthcare-support workflows only. Always keep a qualified human clinician in the loop.

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Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for luizaaca/qwen3-0.6b-clinical-screening

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Finetuned

(1176)

this model