berkeley-nest
/

Starling-RM-7B-alpha

@@ -1,5 +1,5 @@
 ---
-license: cc-by-nc-3.0
 datasets:
 - berkeley-nest/Nectar
 language:
@@ -14,166 +14,32 @@ tags:
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@@ -182,27 +48,3 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
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 ---
+license: cc-by-nc-4.0
 datasets:
 - berkeley-nest/Nectar
 language:
 <!-- Provide a quick summary of what the model is/does. -->
+Starling-RM-7B-alpha is a reward model trained from [Llama2-7B-Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf). Following the tradition of training reward model in the instructGPT paper, we remove the last layer of Llama2-7B Chat,
+and concatenate a linear layer that outputs scalar for any pair of input prompt and response. We train the reward model with preference dataset [berkeley-nest/Nectar](https://huggingface.co/berkeley-nest),
+with the K-wise maximum likelihood estimator proposed in [this paper](https://arxiv.org/abs/2301.11270). The reward model outputs a scalar for any given prompt and response. A response that is more helpful and
+less harmful will get the highest reward score. Note that since the preference dataset [berkeley-nest/Nectar](https://huggingface.co/berkeley-nest) is based on GPT-4 preference, the reward model is likely to be biased
+towards GPT-4's own preference, including longer responses and certain response format.
+- **Developed by:** Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu and Jiantao Jiao.
+- **Model type:** Reward Model for RLHF
+- **License:** Non commercial license
+- **Finetuned from model:** [Llama2-7B-Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
 ### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
+- **Blog:** https://starling.cs.berkeley.edu/
+- **Paper [optional]:** Coming soon!
+- **Code [optional]:** Coming soon!
 ## Uses
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 ## Citation [optional]
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