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---
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- llama
- causal-lm
- code-generation
- lightweight
- 3.08B
base_model:
- Qwen/Qwen2.5-Coder-3B-Instruct
---
<p align="center">
<img alt="HOS-OSS-3.08B" src="https://huggingface.co/hydffgg/HOS-OSS-1.54B/resolve/main/HOS-OSS-270M.png">
</p>
# HOS-OSS-3.08B
HOS-OSS-3.08B is a lightweight 3.08B 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:** ~3.08B parameters
- **Architecture:** LLaMA-style decoder-only transformer
- **Base model:** Qwen2.5-Coder-3B-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-3.08B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
prompt = "User: Write a Python Hello World
Assistant:"
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.7
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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