Instructions to use ise-uiuc/Magicoder-CL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ise-uiuc/Magicoder-CL-7B with Transformers:

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

pipe = pipeline("text-generation", model="ise-uiuc/Magicoder-CL-7B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ise-uiuc/Magicoder-CL-7B")
model = AutoModelForCausalLM.from_pretrained("ise-uiuc/Magicoder-CL-7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ise-uiuc/Magicoder-CL-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ise-uiuc/Magicoder-CL-7B

SGLang

How to use ise-uiuc/Magicoder-CL-7B 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 "ise-uiuc/Magicoder-CL-7B" \
    --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": "ise-uiuc/Magicoder-CL-7B",
		"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 "ise-uiuc/Magicoder-CL-7B" \
        --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": "ise-uiuc/Magicoder-CL-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ise-uiuc/Magicoder-CL-7B with Docker Model Runner:
```
docker model run hf.co/ise-uiuc/Magicoder-CL-7B
```

Judith76 commited on Sep 2

Commit

5d15b27

verified ·

1 Parent(s): 2b87972

Delete model-00005-of-00006.safetensors

Browse files

Hi, thanks for posting this detailed overview of Magicoder-CL-7B. The combination of the OSS-Instruct-75K dataset, the Llama architecture, and the text-generation-inference setup looks really solid for intelligent software engineering tasks. I also appreciate you sharing the arXiv references (2312.02120 and 2305.06161) and the license info—it definitely helps when evaluating models for real-world use. The safetensors file structure seems well organized too.

On a slightly different note, if you ever need a break from all this heavy coding and model fine-tuning, I recently stumbled upon a site that offers a surprisingly fun gaming experience. It’s not AI-related at all, but it's a great way to relax and recharge between training runs. You can check it out right <a href="[https://example.com](https://shadowfight2apkgame.com/)">here</a> if you're interested. I was genuinely impressed by what it offers, and I thought I'd share it as a casual recommendation. Thanks!

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