Instructions to use facebook/opt-350m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/opt-350m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="facebook/opt-350m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use facebook/opt-350m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "facebook/opt-350m" # 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-350m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/facebook/opt-350m
- SGLang
How to use facebook/opt-350m 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-350m" \ --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-350m", "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-350m" \ --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-350m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use facebook/opt-350m with Docker Model Runner:
docker model run hf.co/facebook/opt-350m
Update README.md
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by amyeroberts - opened
README.md
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@@ -55,8 +55,8 @@ You can use this model directly with a pipeline for text generation.
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>>> from transformers import pipeline
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>>> generator = pipeline('text-generation', model="facebook/opt-350m")
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>>> generator("
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[{'generated_text': "
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```
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By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`.
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>>> set_seed(32)
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>>> generator = pipeline('text-generation', model="facebook/opt-350m", do_sample=True)
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>>> generator("
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[{'generated_text': "
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```
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### Limitations and bias
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>>> from transformers import pipeline
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>>> generator = pipeline('text-generation', model="facebook/opt-350m")
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>>> generator("What are we having for dinner?")
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[{'generated_text': "What are we having for dinner?\nI'm having a steak and a salad.\nI'm""}]
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```
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By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`.
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>>> set_seed(32)
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>>> generator = pipeline('text-generation', model="facebook/opt-350m", do_sample=True)
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>>> generator("What are we having for dinner?")
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[{'generated_text': "What are we having for dinner?\n\nWith spring fast approaching, it’s only appropriate"}]
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```
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### Limitations and bias
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