Olmo-3-SFT

About

What Olmo-3-SFT is

Model Details Model Card for Olmo 3 7B Instruct SFT We introduce Olmo 3, a new family of 7B and 32B models both Instruct and Think variants. Long chain-of-thought thinking improves reasoning tasks like math and coding. Olmo is a series of Open language models designed to enable the science of language models. These models are pre-trained on the Dolma 3 dataset and post-trained on the Dolci datasets. We are releasing all code, checkpoints, logs (coming soon), and associated training details. The core models released in this batch include the following: Installation Olmo 3 is supported in transformers 4.57.0 or higher: Inference You can use OLMo with the standard HuggingFace transformers library: For faster performance, you can quantize the model using the following method: The quantized model is more sensitive to data types and CUDA operations. To avoid potential issues, it's recommended to pass the inputs directly to CUDA using: Chat template Default System Message The default system prompt for this model is: Chat Format The chat template for this model is formatted as: Model Description - Developed by: Allen Institute for AI (Ai2) - Model type: a Transformer style autoregressive language model. - Language(s) (NLP): English - License: This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines. - Contact: Technical inquiries: olmo@allenai.org. Press: press@allenai.org - Date cutoff: Dec. 2024. Model Sources - Project Page: https://allenai.org/olmo - Repositories: - Open-Instruct for DPO and RLVR: https://github.com/allenai/open-instruct - OLMo-Core for pre-training and SFT: https://github.com/allenai/OLMo-core - OLMo-Eval for evaluation: https://github.com/allenai/OLMo-Eval - Paper: [TBD] Evaluation Model Details Stage 1: SFT - supervised fine-tuning on the Dolci-Think-SFT-7B dataset. This dataset consits of math, code, chat, and general knowledge queries. - Datasets: Dolci-Think-SFT-7B, Dolci-Instruct-SFT-7B Stage 2:DPO - direct preference optimization on the Dolci-Think-DPO-7B dataset. This dataset consits of math, code, chat, and general knowledge queries. - Datasets: Dolci-Think

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-3-sft for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (olmo-3-sft 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-3-sft","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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