Instructions to use allenai/OLMo-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/OLMo-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="allenai/OLMo-1B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use allenai/OLMo-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "allenai/OLMo-1B" # 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-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/allenai/OLMo-1B
- SGLang
How to use allenai/OLMo-1B 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-1B" \ --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-1B", "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-1B" \ --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-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use allenai/OLMo-1B with Docker Model Runner:
docker model run hf.co/allenai/OLMo-1B
File size: 1,321 Bytes
93e13e7 1e16c6e 93e13e7 8df1740 93e13e7 1e16c6e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | {
"activation_type": "swiglu",
"alibi": false,
"alibi_bias_max": 8.0,
"architectures": [
"OLMoForCausalLM"
],
"attention_dropout": 0.0,
"attention_layer_norm": false,
"attention_layer_norm_with_affine": false,
"bias_for_layer_norm": false,
"block_group_size": 1,
"block_type": "sequential",
"d_model": 2048,
"embedding_dropout": 0.0,
"embedding_size": 50304,
"eos_token_id": 50279,
"flash_attention": false,
"include_bias": false,
"init_cutoff_factor": null,
"init_device": "meta",
"init_fn": "mitchell",
"init_std": 0.02,
"layer_norm_type": "default",
"layer_norm_with_affine": false,
"max_sequence_length": 2048,
"mlp_hidden_size": null,
"mlp_ratio": 8,
"model_type": "hf_olmo",
"multi_query_attention": false,
"n_heads": 16,
"n_layers": 16,
"pad_token_id": 1,
"precision": "amp_bf16",
"residual_dropout": 0.0,
"rope": true,
"rope_full_precision": true,
"scale_logits": false,
"transformers_version": "4.37.1",
"use_cache": true,
"vocab_size": 50280,
"weight_tying": true,
"auto_map": {
"AutoConfig": "configuration_olmo.OLMoConfig",
"AutoModelForCausalLM": "modeling_olmo.OLMoForCausalLM",
"AutoTokenizer": [
"tokenization_olmo_fast.OLMoTokenizerFast",
"tokenization_olmo_fast.OLMoTokenizerFast"
]
}
}
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