Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- FreedomIntelligence/medical-o1-reasoning-SFT
+language:
+- en
+base_model:
+- unsloth/DeepSeek-R1-Distill-Llama-8B
+pipeline_tag: text-generation
+tags:
+- unsloth
+---
+
+# DeepSeek-R1 Medical Reasoning Model
+
+This repository contains a **fine-tuned medical reasoning model** based on  
+[DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B)  
+and trained on the [medical-o1-reasoning-SFT](https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT) dataset.
+
+⚠️ **The uploaded file (`unsloth.Q8_0.gguf`) contains quantized weights** for efficient inference.
+
+---
+
+## 🔍 Model Overview
+
+- **Base Model**: unsloth/DeepSeek-R1-Distill-Llama-8B  
+- **Training Method**: SFT (Supervised Fine-Tuning)  
+- **Domain**: Medical reasoning and clinical knowledge  
+- **Language**: English  
+- **Quantization**: Q8_0 (gguf format for efficient inference)  
+
+---
+
+## 📚 Training Data
+
+The model was fine-tuned on:
+
+- **Dataset**: `FreedomIntelligence/medical-o1-reasoning-SFT`  
+- **Language**: English  
+- **Task**: Medical reasoning, clinical question-answering  
+
+---
+
+## 🚀 Usage Example
+
+> **Note:** The model is stored in `.gguf` format (quantized). You can load it using `unsloth` library.
+
+```python
+from unsloth import FastLanguageModel
+import torch
+
+# Load the quantized GGUF model
+model, tokenizer = FastLanguageModel.from_pretrained(
+    "./unsloth.Q8_0.gguf",
+    max_seq_length=2048,
+    load_in_8bit=True,  # optional depending on quantization
+)
+
+FastLanguageModel.for_inference(model)
+
+def generate(model, prompt, max_new_tokens=200):
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+        )
+
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Example prompt
+prompt = """### Instruction:
+A patient presents with persistent chest pain and shortness of breath. What are possible differential diagnoses?
+
+### Response:
+"""
+
+print(generate(model, prompt))
+