Instructions to use RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits")
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits

SGLang

How to use RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/ise-uiuc_-_Magicoder-S-CL-7B-8bits
```

ise-uiuc_-_Magicoder-S-CL-7B-8bits

File size: 4,891 Bytes

b798e7d

Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

[Discord](https://discord.gg/pvy7H8DZMG)

[Request more models](https://github.com/RichardErkhov/quant_request)


Magicoder-S-CL-7B - bnb 8bits
- Model creator: https://huggingface.co/ise-uiuc/
- Original model: https://huggingface.co/ise-uiuc/Magicoder-S-CL-7B/




Original model description:
---
license: llama2
datasets:
- ise-uiuc/Magicoder-OSS-Instruct-75K
- ise-uiuc/Magicoder-Evol-Instruct-110K
library_name: transformers
pipeline_tag: text-generation
---
# 🎩 Magicoder: Source Code Is All You Need

> Refer to our GitHub repo [ise-uiuc/magicoder](https://github.com/ise-uiuc/magicoder/) for an up-to-date introduction to the Magicoder family!

* 🎩**Magicoder** is a model family empowered by 🪄**OSS-Instruct**, a novel approach to enlightening LLMs with open-source code snippets for generating *low-bias* and *high-quality* instruction data for code.
* 🪄**OSS-Instruct** mitigates the *inherent bias* of the LLM-synthesized instruction data by empowering them with *a wealth of open-source references* to produce more diverse, realistic, and controllable data.

![Overview of OSS-Instruct](assets/overview.svg)
![Overview of Result](assets/result.png)

## Model Details

### Model Description

* **Developed by:**
[Yuxiang Wei](https://yuxiang.cs.illinois.edu),
[Zhe Wang](https://github.com/zhewang2001),
[Jiawei Liu](https://jiawei-site.github.io),
[Yifeng Ding](https://yifeng-ding.com),
[Lingming Zhang](https://lingming.cs.illinois.edu)
* **License:** [Llama 2](https://ai.meta.com/llama/license/)
* **Finetuned from model:** [CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)

### Model Sources

* **Repository:** <https://github.com/ise-uiuc/magicoder>
* **Paper:** <https://arxiv.org/abs/2312.02120>
* **Demo (powered by [Gradio](https://www.gradio.app)):**
<https://github.com/ise-uiuc/magicoder/tree/main/demo>

### Training Data

* [Magicoder-OSS-Instruct-75K](https://huggingface.co/datasets/ise-uiuc/Magicoder_oss_instruct_75k): generated through **OSS-Instruct** using `gpt-3.5-turbo-1106` and used to train both Magicoder and Magicoder-S series.
* [Magicoder-Evol-Instruct-110K](https://huggingface.co/datasets/ise-uiuc/Magicoder_evol_instruct_110k): decontaminated and redistributed from [theblackcat102/evol-codealpaca-v1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1), used to further finetune Magicoder series and obtain Magicoder-S models.

## Uses

### Direct Use

Magicoders are designed and best suited for **coding tasks**.

### Out-of-Scope Use

Magicoders may not work well in non-coding tasks.

## Bias, Risks, and Limitations

Magicoders may sometimes make errors, producing misleading contents, or struggle to manage tasks that are not related to coding.

### Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

## How to Get Started with the Model

Use the code below to get started with the model. Make sure you installed the [transformers](https://huggingface.co/docs/transformers/index) library.

```python
from transformers import pipeline
import torch

MAGICODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.

@@ Instruction
{instruction}

@@ Response
"""

instruction = <Your code instruction here>

prompt = MAGICODER_PROMPT.format(instruction=instruction)
generator = pipeline(
    model="ise-uiuc/Magicoder-S-CL-7B",
    task="text-generation",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
result = generator(prompt, max_length=1024, num_return_sequences=1, temperature=0.0)
print(result[0]["generated_text"])
```

## Technical Details

Refer to our GitHub repo: [ise-uiuc/magicoder](https://github.com/ise-uiuc/magicoder/).

## Citation

```bibtex
@misc{magicoder,
    title={Magicoder: Source Code Is All You Need}, 
    author={Yuxiang Wei and Zhe Wang and Jiawei Liu and Yifeng Ding and Lingming Zhang},
    year={2023},
    eprint={2312.02120},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

## Acknowledgements

* [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder): Evol-Instruct
* [DeepSeek-Coder](https://github.com/deepseek-ai/DeepSeek-Coder): Base model for Magicoder-DS
* [CodeLlama](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/): Base model for Magicoder-CL
* [StarCoder](https://arxiv.org/abs/2305.06161): Data decontamination

## Important Note

Magicoder models are trained on the synthetic data generated by OpenAI models. Please pay attention to OpenAI's [terms of use](https://openai.com/policies/terms-of-use) when using the models and the datasets. Magicoders will not compete with OpenAI's commercial products.