Instructions to use bartowski/Magicoder-S-CL-7B-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/Magicoder-S-CL-7B-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="bartowski/Magicoder-S-CL-7B-exl2")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bartowski/Magicoder-S-CL-7B-exl2", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bartowski/Magicoder-S-CL-7B-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/Magicoder-S-CL-7B-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/Magicoder-S-CL-7B-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bartowski/Magicoder-S-CL-7B-exl2

SGLang

How to use bartowski/Magicoder-S-CL-7B-exl2 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 "bartowski/Magicoder-S-CL-7B-exl2" \
    --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": "bartowski/Magicoder-S-CL-7B-exl2",
		"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 "bartowski/Magicoder-S-CL-7B-exl2" \
        --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": "bartowski/Magicoder-S-CL-7B-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bartowski/Magicoder-S-CL-7B-exl2 with Docker Model Runner:
```
docker model run hf.co/bartowski/Magicoder-S-CL-7B-exl2
```

Exllama v2 Quantizations of Magicoder-S-CL-7B

Using turboderp's ExLlamaV2 v0.0.10 for quantization.

Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.

Conversion was done using Evol-Instruct-Code-80k-v1.parquet as calibration dataset.

Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.

Original model: https://huggingface.co/ise-uiuc/Magicoder-S-CL-7B

Download instructions

With git:

git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/Magicoder-S-CL-7B-exl2

With huggingface hub (credit to TheBloke for instructions):

pip3 install huggingface-hub

To download the main (only useful if you only care about measurement.json) branch to a folder called Magicoder-S-CL-7B-exl2:

mkdir Magicoder-S-CL-7B-exl2
huggingface-cli download bartowski/Magicoder-S-CL-7B-exl2 --local-dir Magicoder-S-CL-7B-exl2 --local-dir-use-symlinks False

To download from a different branch, add the --revision parameter:

mkdir Magicoder-S-CL-7B-exl2
huggingface-cli download bartowski/Magicoder-S-CL-7B-exl2 --revision 4_0 --local-dir Magicoder-S-CL-7B-exl2 --local-dir-use-symlinks False

Downloads last month: -; Downloads are not tracked for this model. How to track

bartowski
/

Magicoder-S-CL-7B-exl2

Exllama v2 Quantizations of Magicoder-S-CL-7B

Download instructions

Datasets used to train bartowski/Magicoder-S-CL-7B-exl2