Instructions to use bartowski/OpenZephyrChat-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bartowski/OpenZephyrChat-exl2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bartowski/OpenZephyrChat-exl2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bartowski/OpenZephyrChat-exl2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bartowski/OpenZephyrChat-exl2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/OpenZephyrChat-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/OpenZephyrChat-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bartowski/OpenZephyrChat-exl2
- SGLang
How to use bartowski/OpenZephyrChat-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/OpenZephyrChat-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/OpenZephyrChat-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/OpenZephyrChat-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/OpenZephyrChat-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bartowski/OpenZephyrChat-exl2 with Docker Model Runner:
docker model run hf.co/bartowski/OpenZephyrChat-exl2
| license: apache-2.0 | |
| library_name: transformers | |
| language: | |
| - en | |
| tags: | |
| - mistral | |
| - merge | |
| - zephyr | |
| - openchat | |
| quantized_by: bartowski | |
| # Exllama v2 Quantizations of OpenZephyrChat at 4.0 bits per weight | |
| Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.10">turboderp's ExLlamaV2 v0.0.10</a> for quantization. | |
| Conversion was done using VMWareOpenInstruct.parquet as calibration dataset. | |
| Original model: https://huggingface.co/Fredithefish/OpenZephyrChat | |
| ## Download instructions | |
| With git: | |
| ```shell | |
| git clone --single-branch --branch 4.0 https://huggingface.co/bartowski/OpenZephyrChat-exl2 | |
| ``` | |
| With huggingface hub (credit to TheBloke for instructions): | |
| ```shell | |
| pip3 install huggingface-hub | |
| ``` | |
| To download from a different branch, add the `--revision` parameter: | |
| ```shell | |
| mkdir OpenZephyrChat-exl2 | |
| huggingface-cli download bartowski/OpenZephyrChat-exl2 --revision 4_0 --local-dir OpenZephyrChat-exl2 --local-dir-use-symlinks False | |
| ``` | |