Update README.md

README.md

@@ -16,7 +16,7 @@ base_model: allenai/OLMoE-1B-7B-0924-SFT
 
 # Model Summary
 
-> 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](https://hf.co/OLMoE/OLMoE-1B-7B-0924). 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.
+> 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](https://hf.co/allenai/OLMoE-1B-7B-0924). 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**](https://github.com/allenai/OLMoE).
 - **Paper**: https://arxiv.org/abs/2409.02060
@@ -52,7 +52,7 @@ Here's how it works: imagine you have a bunch of toys, and you want to
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 Branches:
-- `main`: Preference tuned via DPO model of https://hf.co/OLMoE/OLMoE-1B-7B-0924-SFT (`main` branch)
+- `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.