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Model: luizaaca/qwen3-0.6b-clinical-screening Source: Original Platform
2026-06-05 01:20:16 +08:00
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+---
+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.