---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-30B-A3B-Instruct-2507
tags:
- medical
- case-studies
- japanese
- qwen
- merged
---

# rshaikh22/Qwen3_30B_Medical

This is a merged model combining Qwen/Qwen3-30B-A3B-Instruct-2507 with a LoRA adapter fine-tuned on Japanese medical case studies.

## Model Details

- **Base Model**: Qwen/Qwen3-30B-A3B-Instruct-2507
- **Training Data**: Japanese medical case studies (~93,563 examples)
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation) - Merged
- **Model Type**: Merged Causal LM (no adapter needed)

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("rshaikh22/Qwen3_30B_Medical", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("rshaikh22/Qwen3_30B_Medical", trust_remote_code=True)

# Use the model
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0]))
```

## Training Details

- **Epochs**: 2
- **Learning Rate**: 5e-4
- **Batch Size**: 24
- **Training Examples**: ~93,563