Instructions to use facebook/opt-125m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/opt-125m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="facebook/opt-125m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m") model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use facebook/opt-125m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "facebook/opt-125m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "facebook/opt-125m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/facebook/opt-125m
- SGLang
How to use facebook/opt-125m 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 "facebook/opt-125m" \ --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": "facebook/opt-125m", "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 "facebook/opt-125m" \ --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": "facebook/opt-125m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use facebook/opt-125m with Docker Model Runner:
docker model run hf.co/facebook/opt-125m
Vocab_size of the model configuration is incorrect
#18
by robkirk - opened
In the model configuration for this (and other opt models) the vocab_size is 50272, but the tokenizer has vocab size 50265, which matches the original vocabulary here. and the one on huggingface here. Could this be updated somehow (although I realise that could mess with checkpoints etc.)?
There's this issue on the transformers github referencing the samething.
Hey @robkirk ,
Good question! I think you can find the answer here: https://github.com/huggingface/transformers/issues/17431#issuecomment-1224231170 (it was on another GitHub issue)