UltraRM-13b
Maintainer: openbmb
50
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
The UltraRM-13b model is a reward model developed by the maintainer openbmb and released on the Hugging Face platform. It is trained on the UltraFeedback dataset along with a mixture of other open-source datasets like Anthropic HH-RLHF, Standford SHP, and Summarization. The model is initialized from the LLaMA-13B model and fine-tuned to serve as a reward model for alignment research.
Similar models include UltraLM-13b, a chat language model trained on the UltraChat dataset, and Xwin-LM-13B-V0.1, a powerful, stable, and reproducible LLM alignment model built upon the Llama2 base.
Model inputs and outputs
Inputs
- input_ids: A tensor of token IDs representing the input text.
- attention_mask: An optional tensor indicating which tokens should be attended to.
- position_ids: An optional tensor of position IDs for the input tokens.
- past_key_values: An optional list of cached past key-value states for efficient generation.
- inputs_embeds: An optional tensor of input embeddings.
- labels: An optional tensor of target token IDs for training.
Outputs
- loss: The computed loss value (only returned during training).
- logits: The output logits tensor.
- past_key_values: The past key-value states for efficient generation.
- hidden_states: An optional tuple of the model's output hidden states.
- attentions: An optional tuple of the model's attention weights.
Capabilities
The UltraRM-13b model is a powerful reward model that can be used to facilitate alignment research for large language models. It has been shown to achieve state-of-the-art performance on several public preference test sets, outperforming other open-source reward models. The model's strong performance is attributed to its fine-tuning on a mixture of datasets, including the custom UltraFeedback dataset.
What can I use it for?
The UltraRM-13b model can be used as a reward model for alignment research, helping to train and evaluate large language models to be more reliable, safe, and aligned with human values. Researchers and developers working on improving the safety and reliability of AI systems can use this model to provide rewards and feedback during the training process, helping to steer the model's behavior in a more desirable direction.
Things to try
Researchers can explore fine-tuning the UltraRM-13b model on additional datasets or using it in combination with other alignment techniques, such as inverse reinforcement learning or reward modeling. Developers can also experiment with using the UltraRM-13b model to provide feedback and rewards to their own language models, potentially improving the models' safety and reliability.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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