OLMo

About

What OLMo is

Model Card for OLMo 1B OLMo is a series of Open Language Models designed to enable the science of language models. The OLMo models are trained on the Dolma dataset. We release all code, checkpoints, logs (coming soon), and details involved in training these models. This model has been converted from allenai/OLMo-1B for the Hugging Face Transformers format. Model Details The core models released in this batch are the following: We are releasing many checkpoints for these models, for every 1000 training steps. These have not yet been converted into Hugging Face Transformers format, but are available in allenai/OLMo-1B. Model Description - Developed by: Allen Institute for AI (AI2) - Supported by: Databricks, Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, AMD, CSC (Lumi Supercomputer), UW - Model type: a Transformer style autoregressive language model. - Language(s) (NLP): English - License: The code and model are released under Apache 2.0. - Contact: Technical inquiries: olmo at allenai dot org. Press: press at allenai dot org - Date cutoff: Feb./March 2023 based on Dolma dataset version. Model Sources - Project Page: https://allenai.org/olmo - Repositories: - Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo - Evaluation code: https://github.com/allenai/OLMo-Eval - Further fine-tuning code: https://github.com/allenai/open-instruct - Paper: Link - Technical blog post: https://blog.allenai.org/olmo-open-language-model-87ccfc95f580 - W&B Logs: https://wandb.ai/ai2-llm/OLMo-1B/reports/OLMo-1B--Vmlldzo2NzY1Njk1 Uses Inference Quickly get inference running with the following: Alternatively, with the pipeline abstraction: Or, you can make this slightly faster by quantizing the model, e.g. AutoModelForCausalLM.frompretrained("allenai/OLMo-1B-hf", torchdtype=torch.float16, loadin8bit=True) (requires bitsandbytes). The quantized model is more sensitive to typing / cuda, so it is recommended to pass the inputs as inputs.inputids.to('cuda') to avoid potential issues. Fine-tuning This model does not directly support our fine-tuning processes. Model fine-tuning can be done from the final checkpoint

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys olmo for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (olmo below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"olmo","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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