Instructions to use BigSalmon/GPTNeo350MInformalToFormalLincoln6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigSalmon/GPTNeo350MInformalToFormalLincoln6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BigSalmon/GPTNeo350MInformalToFormalLincoln6")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln6") model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo350MInformalToFormalLincoln6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BigSalmon/GPTNeo350MInformalToFormalLincoln6 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BigSalmon/GPTNeo350MInformalToFormalLincoln6" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BigSalmon/GPTNeo350MInformalToFormalLincoln6", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BigSalmon/GPTNeo350MInformalToFormalLincoln6
- SGLang
How to use BigSalmon/GPTNeo350MInformalToFormalLincoln6 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 "BigSalmon/GPTNeo350MInformalToFormalLincoln6" \ --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": "BigSalmon/GPTNeo350MInformalToFormalLincoln6", "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 "BigSalmon/GPTNeo350MInformalToFormalLincoln6" \ --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": "BigSalmon/GPTNeo350MInformalToFormalLincoln6", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BigSalmon/GPTNeo350MInformalToFormalLincoln6 with Docker Model Runner:
docker model run hf.co/BigSalmon/GPTNeo350MInformalToFormalLincoln6
Download pytorch_model.bin from BigSalmon/GPTNeo350MInformalToFormalLincoln6: direct link, hf CLI and curl.
- Browser
- Download file 1.52 GB
-
https://huggingface.co/BigSalmon/GPTNeo350MInformalToFormalLincoln6/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://BigSalmon/GPTNeo350MInformalToFormalLincoln6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/BigSalmon/GPTNeo350MInformalToFormalLincoln6/resolve/main/pytorch_model.bin
1.52 GB
- Xet hash:
- 6a8abcd4c26b300b50b1f554807a48fd276ab0f90cc00b80b5e40ece7f3aca6f
- Size of remote file:
- 1.52 GB
- SHA256:
- 076ca1dca24cd83aa885e30ff9b12adb87451397a1888aff94fcd2b835d5f389
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