kingabzpro
/

Qwen-3-32B-Medical-Reasoning

@@ -1,199 +1,167 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+tags:
+- medical
+license: apache-2.0
+datasets:
+- FreedomIntelligence/medical-o1-reasoning-SFT
+language:
+- en
+base_model:
+- Qwen/Qwen3-32B
+pipeline_tag: text-generation
 ---
+# Fine-tuning Qwen3-32B in 4-bit Quantization for Medical Reasoning
+This project fine-tunes the [`Qwen/Qwen3-32B`](https://huggingface.co/Qwen/Qwen3-32B) model using a medical reasoning dataset (`FreedomIntelligence/medical-o1-reasoning-SFT`) with **4-bit quantization** for memory-efficient training.
+---
+## Setup
+1. Install the required libraries:
+```bash
+pip install -U datasets accelerate peft trl bitsandbytes
+pip install -U transformers
+pip install huggingface_hub[hf_xet]
+```
+2. Authenticate with Hugging Face Hub:
+Make sure your Hugging Face token is stored in an environment variable:
+```bash
+export HF_TOKEN=your_huggingface_token
+```
+The notebook will automatically log you in using this token.
+---
+## How to Run
+1. **Load the Model and Tokenizer**
+   The script downloads the Qwen3-32B model and applies 4-bit quantization with `BitsAndBytesConfig` for efficient memory usage.
+2. **Prepare the Dataset**
+   - The notebook uses `FreedomIntelligence/medical-o1-reasoning-SFT` (first 500 samples).
+   - It formats each example into an **instruction-following prompt** with step-by-step chain-of-thought reasoning.
+3. **Fine-tuning**
+   - Fine-tuning is set up with PEFT (LoRA / Adapter Tuning style) to modify a small subset of model parameters.
+   - TRL (Transformer Reinforcement Learning) is used to fine-tune efficiently.
+4. **Push Fine-tuned Model**
+   - After training, the fine-tuned model and tokenizer are pushed back to your Hugging Face account.
+---
+Here is the training notebook: [Fine_tuning_Qwen-3-32B](https://huggingface.co/kingabzpro/Qwen-3-32B-Medical-Reasoning/blob/main/fine-tuning-qwen-3.ipynb)
+## Model Configuration
+- **Base Model**: `Qwen/Qwen3-32B`
+- **Quantization**: 4-bit (NF4)
+- **Training**: PEFT + TRL
+- **Dataset**: 2000 examples from medical reasoning dataset
+---
+## Notes
+- **GPU Required**: Make sure you have access to 1X A100s. Get it from RunPod for an hours. Training took only 50 minutes.
+- **Environment**: The notebook expects an environment where NVIDIA CUDA drivers are available (`nvidia-smi` check is included).
+- **Memory Efficiency**: 4-bit loading greatly reduces memory footprint.
+---
+## Example Prompt Format
+```
+Below is an instruction that describes a task, paired with an input that provides further context.
+Write a response that appropriately completes the request.
+Before answering, think carefully about the question and create a step-by-step chain of thoughts to ensure a logical and accurate response.
+### Instruction:
+You are a medical expert with advanced knowledge in clinical reasoning, diagnostics, and treatment planning.
+Please answer the following medical question.
+### Question:
+{}
+### Response:
+<think>
+{}
+</think>
+{}
+```
+---
+## Usage Script (not-tested)
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+import torch
+# Base model (original model from Meta)
+base_model_id = "Qwen/Qwen3-32B"
+# Your fine-tuned LoRA adapter repository
+lora_adapter_id = "kingabzpro/Qwen-3-32B-Medical-Reasoning"
+# Load the model in 4-bit
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=False,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    quantization_config=bnb_config,
+    trust_remote_code=True,
+)
+# Attach the LoRA adapter
+model = PeftModel.from_pretrained(
+    base_model,
+    lora_adapter_id,
+    device_map="auto",
+    trust_remote_code=True,
+)
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
+# Inference example
+prompt = """Below is an instruction that describes a task, paired with an input that provides further context.
+Write a response that appropriately completes the request.
+Before answering, think carefully about the question and create a step-by-step chain of thoughts to ensure a logical and accurate response.
+### Instruction:
+You are a medical expert with advanced knowledge in clinical reasoning, diagnostics, and treatment planning.
+Please answer the following medical question.
+### Question:
+What is the initial management for a patient presenting with diabetic ketoacidosis (DKA)?
+### Response:
+<think>
+"""
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=1200)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```