metadata
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
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