Instructions to use vicgalle/gpt-j-6B-alpaca-gpt4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vicgalle/gpt-j-6B-alpaca-gpt4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="vicgalle/gpt-j-6B-alpaca-gpt4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("vicgalle/gpt-j-6B-alpaca-gpt4") model = AutoModelForCausalLM.from_pretrained("vicgalle/gpt-j-6B-alpaca-gpt4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use vicgalle/gpt-j-6B-alpaca-gpt4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "vicgalle/gpt-j-6B-alpaca-gpt4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vicgalle/gpt-j-6B-alpaca-gpt4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/vicgalle/gpt-j-6B-alpaca-gpt4
- SGLang
How to use vicgalle/gpt-j-6B-alpaca-gpt4 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 "vicgalle/gpt-j-6B-alpaca-gpt4" \ --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": "vicgalle/gpt-j-6B-alpaca-gpt4", "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 "vicgalle/gpt-j-6B-alpaca-gpt4" \ --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": "vicgalle/gpt-j-6B-alpaca-gpt4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use vicgalle/gpt-j-6B-alpaca-gpt4 with Docker Model Runner:
docker model run hf.co/vicgalle/gpt-j-6B-alpaca-gpt4
Download pytorch_model-00002-of-00003.bin from vicgalle/gpt-j-6B-alpaca-gpt4: direct link, hf CLI and curl.
- Browser
- Download file 9.98 GB
-
https://huggingface.co/vicgalle/gpt-j-6B-alpaca-gpt4/resolve/75833bb49ac5bb68cf1f40a7e9865108c8ff91cd/pytorch_model-00002-of-00003.bin
- Command line
-
hf download hf://vicgalle/gpt-j-6B-alpaca-gpt4@75833bb49ac5bb68cf1f40a7e9865108c8ff91cd/pytorch_model-00002-of-00003.bin
-
curl -L -o pytorch_model-00002-of-00003.bin https://huggingface.co/vicgalle/gpt-j-6B-alpaca-gpt4/resolve/75833bb49ac5bb68cf1f40a7e9865108c8ff91cd/pytorch_model-00002-of-00003.bin
9.98 GB
- Xet hash:
- 6f0aeb328b3f226e3730a71ea9249484733007feea0968b9b0df1be8b2acec57
- Size of remote file:
- 9.98 GB
- SHA256:
- 9d852e2962ec3691a32bb3cb4ffbd7100b6e98888229bcc113ebdb849f4992be
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