Instructions to use bartowski/Magicoder-S-CL-7B-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Main branch
Browse files- README.md +49 -0
- measurement.json +0 -0
README.md
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---
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license: llama2
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datasets:
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- ise-uiuc/Magicoder-OSS-Instruct-75K
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- ise-uiuc/Magicoder-Evol-Instruct-110K
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library_name: transformers
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pipeline_tag: text-generation
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quantized_by: bartowski
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---
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## Exllama v2 Quantizations of Magicoder-S-CL-7B
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Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.10">turboderp's ExLlamaV2 v0.0.10</a> for quantization.
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Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
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Conversion was done using Evol-Instruct-Code-80k-v1.parquet as calibration dataset.
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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.
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Original model: https://huggingface.co/ise-uiuc/Magicoder-S-CL-7B
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## Download instructions
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With git:
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```shell
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git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/Magicoder-S-CL-7B-exl2
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```
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With huggingface hub (credit to TheBloke for instructions):
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```shell
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pip3 install huggingface-hub
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```
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To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Magicoder-S-CL-7B-exl2`:
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```shell
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mkdir Magicoder-S-CL-7B-exl2
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huggingface-cli download bartowski/Magicoder-S-CL-7B-exl2 --local-dir Magicoder-S-CL-7B-exl2 --local-dir-use-symlinks False
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```
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To download from a different branch, add the `--revision` parameter:
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```shell
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mkdir Magicoder-S-CL-7B-exl2
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huggingface-cli download bartowski/Magicoder-S-CL-7B-exl2 --revision 4_0 --local-dir Magicoder-S-CL-7B-exl2 --local-dir-use-symlinks False
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```
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measurement.json
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