Model overview
> OLMoE-1B-7B-Instruct is a Mixture-of-Experts LLM with 1B active and 7B total parameters released in September 2024 (0924) that has been adapted via SFT and DPO from OLMoE-1B-7B. It yields state-of-the-art performance among models with a similar cost (1B) and is competitive with much larger models like Llama2-13B-Chat. OLMoE is 100% open-source. This information and more can also be found on the **OLMoE GitHub repository**. - **Paper**: https://arxiv.org/abs/2409.02060 - **Pretraining** Checkpoints, Code, Data and Logs. - **SFT (Supervised Fine-Tuning)** Checkpoints, Code, Data and Logs. - **DPO/KTO (Direct Preference Optimization/Kahneman-Tversky Optimization)**, Checkpoints, Preference Data, DPO code, KTO code and Logs. Branches: - `main`: Preference tuned via DPO model of https://hf.co/allenai/OLMoE-1B-7B-0924-SFT (`main` branch) - `load-balancing`: Ablation with load balancing loss during DPO starting from the `load-balancing` branch of https://hf.co/allenai/OLMoE-1B-7B-0924-SFT - `non-annealed`: Ablation starting from the `non-annealed` branch of https://hf.co/allenai/OLMoE-1B-7B-0924-SFT which is an SFT of the pretraining checkpoint prior to annealing (branch `step1200000-tokens5033B` of https://hf.co/allenai/OLMoE-1B-7B-0924) - `kto`: Ablation using KTO instead of DPO. This branch is the checkpoint after 5,000 steps with the RMS optimizer. The other `kto*` branches correspond to the other checkpoints mentioned in the paper.
Key features
特点摘要待依据来源补齐,不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
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Sources & traceability
许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。