Update README.md

README.md

@@ -1,14 +1,90 @@
 ---
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - text-generation
+  - medical
+  - loRA
+  - 4bit
 base_model: deepseek-ai/DeepSeek-V2-Lite
-library_name: peft
 ---
 
+# DeepSeek-V2-medical
+
+This repository contains a 4-bit LoRA fine-tuned adapter on top of [deepseek-ai/DeepSeek-V2-Lite](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite) for medical treatment planning.
+
+## Model Card
+
+- **Base model:** `deepseek-ai/DeepSeek-V2-Lite` (4-bit quantized)  
+- **Adapter:** LoRA, trained on clinical vignette→treatment pairs  
+- **Tokenizer:** same as base, with pad_token set to eos  
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, BitsAndBytesConfig
+from peft        import PeftModel
+import torch
+
+# 1) Load tokenizer + adapter
+tokenizer = AutoTokenizer.from_pretrained(
+    "CodCodingCode/DeepSeek-V2-medical", trust_remote_code=True
+)
+tokenizer.pad_token_id = tokenizer.pad_token_id or tokenizer.eos_token_id
+
+# 2) Reload quantized base
+bnb = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+)
+base = AutoModelForCausalLM.from_pretrained(
+    "deepseek-ai/DeepSeek-V2-Lite",
+    quantization_config=bnb,
+    device_map="auto",
+    trust_remote_code=True,
+)
+base.resize_token_embeddings(len(tokenizer))
+
+# 3) Attach LoRA adapter
+model = PeftModel.from_pretrained(
+    base,
+    "CodCodingCode/DeepSeek-V2-medical",
+    device_map="auto",
+    trust_remote_code=True,
+)
+model.config.use_cache = False
+
+# 4) Generate text
+prompt = (
+    "### Instruction:\n"
+    "You are a board-certified clinician. Based on the following patient vignette, "
+    "suggest a concise treatment plan:\n\n"
+    "### Input:\n"
+    "A 65-year-old presents with chronic shortness of breath and persistent cough...\n\n"
+    "### Response:\n"
+)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=128,
+    do_sample=True,
+    top_p=0.9,
+    temperature=0.8,
+    pad_token_id=tokenizer.pad_token_id,
+    eos_token_id=tokenizer.eos_token_id,
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
 
 
 
+
 ## Model Details
 
 ### Model Description