Gemma-2-9B-It-SPPO-Iter3
Maintainer: UCLA-AGI
92
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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
The Gemma-2-9B-It-SPPO-Iter3 model is a 9B parameter GPT-like model developed by UCLA-AGI using Self-Play Preference Optimization at iteration 3. It is based on the google/gemma-2-9b-it architecture as a starting point. The model was trained on synthetic datasets from the openbmb/UltraFeedback and snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset datasets.
The Gemma-2-9B-It-SPPO-Iter3 model is part of a series of models developed by UCLA-AGI using the Self-Play Preference Optimization technique, with the Gemma-2-9B-It-SPPO-Iter1 and Gemma-2-9B-It-SPPO-Iter2 models also available.
Model inputs and outputs
Inputs
- Text prompts or instructions in English
Outputs
- Text responses generated in English
Capabilities
The Gemma-2-9B-It-SPPO-Iter3 model is capable of a variety of text generation tasks such as question answering, summarization, and creative writing. It has shown strong performance on the AlpacaEval leaderboard, with a winning rate of 47.74% and an average response length of 1803 tokens.
What can I use it for?
The Gemma-2-9B-It-SPPO-Iter3 model can be used for a wide range of natural language processing applications, including chatbots, content creation, and research. Developers can fine-tune the model on specific datasets or tasks to adapt it to their needs. The model's open-source nature and relatively small size make it accessible for deployment on limited-resource environments like laptops or desktops.
Things to try
One interesting aspect of the Gemma-2-9B-It-SPPO-Iter3 model is its ability to generate coherent and contextual responses across multiple turns of a conversation. Developers can experiment with using the provided chat template to create interactive conversational experiences. Additionally, the model's strong performance on the AlpacaEval leaderboard suggests it may be well-suited for tasks that require following instructions and providing relevant, high-quality responses.
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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