LLaMA-3-8B-SFR-Iterative-DPO-R
Maintainer: Salesforce
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| Property | Value |
|---|---|
| Run this model | Run 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
LLaMA-3-8B-SFR-Iterative-DPO-R is a state-of-the-art instruct model developed by Salesforce. It outperforms similar-sized models, most large open-sourced models, and strong proprietary models on three widely-used instruct model benchmarks: Alpaca-Eval-V2, MT-Bench, and Chat-Arena-Hard. The model is trained on open-sourced datasets without additional human or GPT4 labeling.
The SFR-Iterative-DPO-LLaMA-3-8B-R model follows a similar approach, also outperforming other models on these benchmarks. Salesforce has developed an efficient online RLHF recipe for LLM instruct training, using a DPO-based method that is cheaper and simpler to train than PPO-based approaches.
Model Inputs and Outputs
Inputs
- Text prompts
Outputs
- Generated text responses
Capabilities
The LLaMA-3-8B-SFR-Iterative-DPO-R model has shown strong performance on a variety of instruct model benchmarks. It can engage in open-ended conversations, answer questions, and complete tasks across a wide range of domains.
What Can I Use It For?
The LLaMA-3-8B-SFR-Iterative-DPO-R model can be used for building conversational AI assistants, automating text-based workflows, and generating content. Potential use cases include customer service, technical support, content creation, and task completion. As with any large language model, developers should carefully consider safety and ethical implications when deploying the model.
Things to Try
Try prompting the model with specific tasks or open-ended questions to see its versatility and capabilities. You can also experiment with different generation parameters, such as temperature and top-p, to control the model's output. Additionally, consider fine-tuning the model on your own data to adapt it to your specific use case.
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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