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    ContentProducer: Minimax Agent AI
    ContentPropagator: Minimax Agent AI
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    PropagateID: f3e961de220519135b7936401f9c497b
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---

# shenwen-coderV2-Instruct

![Hugging Face](https://huggingface.co/front/assets/huggingface\_logo.svg)

[![Model](https://img.shields.io/badge/Model-shenwen--coderV2--Instruct-blue.svg)](https://huggingface.co/shenwenAI/shenwen-coderV2-Instruct)[![Format](https://img.shields.io/badge/Format-Safetensors-green.svg)](https://huggingface.co/shenwenAI/shenwen-coderV2-Instruct)[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://huggingface.co/shenwenAI/shenwen-coderV2-Instruct)

## Model Overview

**shenwen-coderV2-Instruct** is an instruction-tuned code generation model based on Qwen2.5-Coder-0.5B-Instruct, optimized for various code generation tasks.

## Model Details

- **Base Model**: Qwen2.5-Coder-0.5B-Instruct
- **Tensor Type**: BF16
- **Parameters**: 0.5B
- **Architecture**: qwen2

## Usage

### Using Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "shenwenAI/shenwen-coderV2-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Write a Python function to calculate factorial:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Using vLLM

```python
from vllm import LLM, SamplingParams

llm = LLM(model="shenwenAI/shenwen-coderV2-Instruct")
sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=512)

prompts = ["Write a Python function to calculate factorial:"]
outputs = llm.generate(prompts, sampling_params)
print(outputs[0].outputs[0].text)
```

## Usage with swllm.cpp (Optimized Code Generation)

For optimized code generation, we recommend using our custom **swllm.cpp** tool:

```bash
# Clone swllm.cpp
git clone https://github.com/shenwenAI/swllm.cpp
cd swllm.cpp

# Build with this model
# Convert model to GGUF format first if needed

# Run inference
./build/bin/swllm-cli -m path/to/model.gguf -n 512 -p "Write a Python function to calculate factorial:"
```

**swllm.cpp** provides optimized code generation capabilities for enhanced performance and quality.

## Quantization

For quantized versions, please visit: [shenwenAI/shenwen-coderV2-GGUF](https://huggingface.co/shenwenAI/shenwen-coderV2-GGUF)

| Quantization | Size |
| --- | --- |
| Q2_K | 339 MB |
| Q4_K_M | 398 MB |
| Q5_K_M | 420 MB |
| Q8_0 | 531 MB |
| F16 | 994 MB |

## License

Apache 2.0 - See [LICENSE](https://huggingface.co/shenwenAI/shenwen-coderV2-Instruct/blob/main/LICENSE)

## Acknowledgments

- [Qwen Team](https://github.com/QwenLM/Qwen) for Qwen2.5-Coder
- [shenwenAI](https://huggingface.co/shenwenAI) for model training and optimization

## Connect With Us

- **GitHub**: [https://github.com/shenwenAI](https://github.com/shenwenAI)
- **HuggingFace**: [https://huggingface.co/shenwenAI](https://huggingface.co/shenwenAI)
- **Twitter/X**: [https://x.com/shenwenai](https://x.com/shenwenai)

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*If this model is helpful, please consider giving us a star on GitHub and following us on social media!*