---
license: cc-by-4.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
base_model: Qwen/Qwen3-0.6B
base_model_relation: finetune
datasets:
- dhivyeshrk/diseases-and-symptoms-dataset
- niyarrbarman/symptom2disease
tags:
- 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`](https://github.com/luizaaca/screening_robot/blob/main/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:

  ```text
  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:

  ```text
  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.