Instructions to use facebook/opt-66b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/opt-66b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="facebook/opt-66b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("facebook/opt-66b") model = AutoModelForCausalLM.from_pretrained("facebook/opt-66b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use facebook/opt-66b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "facebook/opt-66b" # 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-66b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/facebook/opt-66b
- SGLang
How to use facebook/opt-66b 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-66b" \ --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-66b", "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-66b" \ --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-66b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use facebook/opt-66b with Docker Model Runner:
docker model run hf.co/facebook/opt-66b
Add evaluation results on the mathemakitten--winobias_antistereotype_test config and test split of mathemakitten/winobias_antistereotype_test
Browse filesBeep boop, I am a bot from Hugging Face's automatic model evaluator 👋!\
Your model has been evaluated on the mathemakitten--winobias_antistereotype_test config and test split of the [mathemakitten/winobias_antistereotype_test](https://huggingface.co/datasets/mathemakitten/winobias_antistereotype_test) dataset by @mathemakitten , using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test-mathemakitt-c50da3-1597456336).\
Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=mathemakitten/winobias_antistereotype_test).\
Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=mathemakitten/winobias_antistereotype_test).
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tags:
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- text-generation
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- opt
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-
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license: other
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commercial: false
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---
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# OPT : Open Pre-trained Transformer Language Models
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tags:
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- text-generation
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- opt
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license: other
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commercial: false
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model-index:
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- name: facebook/opt-66b
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results:
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- task:
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type: zero-shot-classification
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name: Zero-Shot Text Classification
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dataset:
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name: mathemakitten/winobias_antistereotype_test
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type: mathemakitten/winobias_antistereotype_test
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config: mathemakitten--winobias_antistereotype_test
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split: test
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.3907766990291262
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verified: true
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- name: Loss
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type: loss
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value: 1.5451246622052626
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verified: true
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---
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# OPT : Open Pre-trained Transformer Language Models
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