license, language, library_name, pipeline_tag, base_model, base_model_relation, datasets, tags

license	language	library_name	pipeline_tag	base_model	base_model_relation	datasets	tags
cc-by-4.0	en	transformers	text-generation	Qwen/Qwen3-0.6B	finetune	dhivyeshrk/diseases-and-symptoms-dataset niyarrbarman/symptom2disease	medical clinical-screening symptom-to-disease disease-classification lora qlora gguf unsloth peft transformers bertscore

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.