Instructions to use allenai/OLMo-7B-0424-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/OLMo-7B-0424-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="allenai/OLMo-7B-0424-hf")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-7B-0424-hf") model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-0424-hf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use allenai/OLMo-7B-0424-hf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "allenai/OLMo-7B-0424-hf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "allenai/OLMo-7B-0424-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/allenai/OLMo-7B-0424-hf
- SGLang
How to use allenai/OLMo-7B-0424-hf 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 "allenai/OLMo-7B-0424-hf" \ --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": "allenai/OLMo-7B-0424-hf", "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 "allenai/OLMo-7B-0424-hf" \ --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": "allenai/OLMo-7B-0424-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use allenai/OLMo-7B-0424-hf with Docker Model Runner:
docker model run hf.co/allenai/OLMo-7B-0424-hf
Update README.md
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README.md
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@@ -75,7 +75,7 @@ Proceed as usual with HuggingFace:
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from transformers import AutoModelForCausalLM, AutoTokenizer
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olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-0424-hf")
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tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-7B-0424-hf")
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message = ["Language modeling is
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inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
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# optional verifying cuda
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# inputs = {k: v.to('cuda') for k,v in inputs.items()}
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from transformers import AutoModelForCausalLM, AutoTokenizer
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olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-0424-hf")
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tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-7B-0424-hf")
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message = ["Language modeling is"]
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inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
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# optional verifying cuda
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# inputs = {k: v.to('cuda') for k,v in inputs.items()}
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