Instructions to use allenai/open-instruct-self-instruct-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/open-instruct-self-instruct-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="allenai/open-instruct-self-instruct-7b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("allenai/open-instruct-self-instruct-7b") model = AutoModelForCausalLM.from_pretrained("allenai/open-instruct-self-instruct-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use allenai/open-instruct-self-instruct-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "allenai/open-instruct-self-instruct-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "allenai/open-instruct-self-instruct-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/allenai/open-instruct-self-instruct-7b
- SGLang
How to use allenai/open-instruct-self-instruct-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 "allenai/open-instruct-self-instruct-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": "allenai/open-instruct-self-instruct-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 "allenai/open-instruct-self-instruct-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": "allenai/open-instruct-self-instruct-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use allenai/open-instruct-self-instruct-7b with Docker Model Runner:
docker model run hf.co/allenai/open-instruct-self-instruct-7b
Download pytorch_model-00001-of-00003.bin from allenai/open-instruct-self-instruct-7b: direct link, hf CLI and curl.
- Browser
- Download file 9.88 GB
-
https://huggingface.co/allenai/open-instruct-self-instruct-7b/resolve/c0fafb1a93f3af528eb5b4a3b2b5d737898fe687/pytorch_model-00001-of-00003.bin
- Command line
-
hf download hf://allenai/open-instruct-self-instruct-7b@c0fafb1a93f3af528eb5b4a3b2b5d737898fe687/pytorch_model-00001-of-00003.bin
-
curl -L -o pytorch_model-00001-of-00003.bin https://huggingface.co/allenai/open-instruct-self-instruct-7b/resolve/c0fafb1a93f3af528eb5b4a3b2b5d737898fe687/pytorch_model-00001-of-00003.bin
9.88 GB
- Xet hash:
- 1ad412a7c5259645b9a7e0674c3ede475c34acc21d28ee6cb09b0a5663df4572
- Size of remote file:
- 9.88 GB
- SHA256:
- fbe01b90e082869667f01feaadd9be47e7e5a5f56551e1275bbed17a5be5c84d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.