Update README.md

README.md

@@ -2,43 +2,15 @@
 library_name: transformers
 license: apache-2.0
 base_model: Qwen/Qwen3-30B-A3B-Instruct-2507
-tags:
-- medical
-- case-studies
-- japanese
-- qwen
-- merged
+language:
+- en
 ---
 
-# rshaikh22/Qwen3_30B_Instruct_CQA_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_Instruct_CQA_Medical", trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained("rshaikh22/Qwen3_30B_Instruct_CQA_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
+- **Training Examples**: ~93,563