Instructions to use abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq") model = AutoModelForCausalLM.from_pretrained("abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq
- SGLang
How to use abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq 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 "abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq" \ --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": "abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq", "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 "abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq" \ --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": "abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq with Docker Model Runner:
docker model run hf.co/abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq
Abhinav Kulkarni commited on
Commit ·
ea43866
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Parent(s): e5f26b0
Updated README
Browse files
README.md
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This evaluation was done using [LM-Eval](https://github.com/EleutherAI/lm-evaluation-harness).
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This evaluation was done using [LM-Eval](https://github.com/EleutherAI/lm-evaluation-harness).
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[StableBeluga-7B](https://huggingface.co/stabilityai/StableBeluga-7B)
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[StableBeluga-7B (4-bit 128-group AWQ)](https://huggingface.co/abhinavkulkarni/stabilityai-StableBeluga-7B-w4-g128-awq)
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