Update README.md

README.md

@@ -2,57 +2,6 @@
 library_name: transformers
 license: apache-2.0
 base_model: Qwen/Qwen3-30B-A3B-Instruct-2507
-tags:
-- medical
-- case-studies
-- japanese
-- qwen
-- lora
----
-
-# rshaikh22/Qwen3_30B_Instruct_CQA_Medical
-
-This is a LoRA (Low-Rank Adaptation) fine-tuned version of Qwen/Qwen3-30B-A3B-Instruct-2507 trained 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)
-- **Model Type**: LoRA Adapter (requires base model to load)
-
-## Usage
-
-### Using with PEFT (Recommended)
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-
-# Load base model
-base_model = AutoModelForCausalLM.from_pretrained(
-    "Qwen/Qwen3-30B-A3B-Instruct-2507", 
-    trust_remote_code=True,
-    torch_dtype="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-30B-A3B-Instruct-2507", trust_remote_code=True)
-
-# Load LoRA adapter
-model = PeftModel.from_pretrained(base_model, "rshaikh22/Qwen3_30B_Instruct_CQA_Medical")
-model = model.merge_and_unload()  # Optional: merge adapter into base model
-
-# 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]))
-```
-
-### Direct Loading (if adapter is merged)
-
-```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)
-```
+language:
+- en
+---