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 / README.md

luizaaca

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metadata

license: cc-by-4.0
language:
  - en
library_name: peft
pipeline_tag: text-generation
base_model: Qwen/Qwen3-1.7B-Base
base_model_relation: finetune
datasets:
  - luizaaca/symptoms-to-diseases-with-reasoning
tags:
  - medical
  - clinical-screening
  - symptom-analysis
  - structured-output
  - json
  - lora
  - qlora
  - gguf
  - llama.cpp
  - ollama
  - unsloth
  - peft
  - transformers

Qwen3-1.7B Clinical Screening

This repository packages the artifacts generated by the training notebook screening_robot_qwen3_1_7b_json.ipynb for the symptom_analysis specialist step of the Screening Robot workflow.

Artifact layout

lora/: PEFT LoRA adapter weights and tokenizer/chat-template files.
gguf/qwen3-1.7b-clinical-screening.Q4_K_M.gguf: merged and quantized GGUF export (Q4_K_M) for llama.cpp and Ollama-style runtimes.
gguf/config.json, gguf/tokenizer.json, gguf/tokenizer_config.json, gguf/chat_template.jinja: support files exported alongside the GGUF build.
Modelfile: Ollama-oriented template aligned with the same JSON-focused system prompt used in the training notebook.

Model details

Repository: https://huggingface.co/luizaaca/qwen3-1.7b-clinical-screening
Base model: Qwen/Qwen3-1.7B-Base
Training runtime base: unsloth/qwen3-1.7b-base-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 runtime: Google Colab + Tesla T4 (16 GB VRAM)
Kaggle Dataset: luizaaca/symptoms-to-diseases-with-reasoning

Intended behavior

The model was fine-tuned to emit a compact JSON object with exactly three keys:

support_status
candidate_diseases
recommended_exams_tests

The fine-tuning notebook explicitly scoped the model to the dataset-aligned specialist contract used by the symptom_analysis node. It does not represent the full downstream production payload used elsewhere in the application.

Prompt contract

Training and validation were built around the following behavior:

a clinical screening system prompt focused on symptom analysis;
a user message formatted as Clinical request plus Active patient context; and
an assistant answer restricted to JSON only.

The default Modelfile included in this repository mirrors that setup.

Validation summary

The notebook includes:

a held-out evaluation pass comparing the fine-tuned model against the base model;
an assertion that fine-tuned label accuracy matches or exceeds the base model on the evaluation split; and
GGUF smoke tests that require the exported model to emit valid schema-compliant JSON.

No claim of clinical validation, diagnostic safety, or regulatory readiness is made.

Notes about the GGUF filename

The local GGUF export produced by the toolchain may inherit the original base model filename. In this repository the uploaded GGUF weight is renamed to qwen3-1.7b-clinical-screening.Q4_K_M.gguf to make it explicit that the file contains the fine-tuned clinical screening variant.

Intended use

This repository is suitable for:

research experiments on structured clinical-screening assistants;
integration prototypes for symptom-intake workflows; and
local inference with PEFT or GGUF-compatible runtimes.

Out-of-scope use

This repository is not intended for:

autonomous diagnosis or treatment decisions;
emergency or high-acuity 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 screening-assistance workflows only. Always keep a qualified human clinician in the loop.