Update README.md

README.md

@@ -7,11 +7,63 @@ pipeline_tag: text-generation
 tags:
 - llama
 - causal-lm
+- code-generation
 - 270m
+- lightweight
 base_model:
 - Qwen/Qwen2.5-Coder-1.5B-Instruct
 ---
 
 # HOS-OSS-270M
 
-Custom 270M LLaMA-style model.
+HOS-OSS-270M is a lightweight 270M parameter causal language model optimized for text and code generation tasks.  
+It is designed for fast inference, low resource usage, and local deployment.
+
+---
+
+## 🚀 Overview
+
+- **Model size:** ~270M parameters  
+- **Architecture:** LLaMA-style decoder-only transformer  
+- **Base model:** Qwen2.5-Coder-1.5B-Instruct (distilled / adapted)  
+- **Framework:** 🤗 Transformers  
+- **Use cases:**  
+  - Code generation  
+  - Instruction following  
+  - Chat-style completion  
+  - Lightweight local AI assistant  
+
+---
+
+## ⚡ Features
+
+- Fast inference on low-end GPUs
+- Runs on Kaggle / Colab without large VRAM
+- Suitable for edge deployment
+- Clean instruction-response formatting
+
+---
+
+## 🧠 Example Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_name = "hydffgg/HOS-OSS-270M"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+prompt = "User: Write a Python Hello World\nAssistant:"
+
+inputs = tokenizer(prompt, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=100,
+        temperature=0.7
+    )
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))