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:
1Based on the reported symptoms, the clinical indication points to: <disease>.
2Disclaimer: 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:
1Given the symptoms reported, identify the disease.
2
3Symptoms: ... /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.
