Llama-3-Instruct-8B-SPPO-Iter3
Maintainer: UCLA-AGI
71
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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-Instruct-8B-SPPO-Iter3 is a large language model developed by UCLA-AGI using the Self-Play Preference Optimization technique. It is based on the Meta-Llama-3-8B-Instruct architecture and was fine-tuned on synthetic datasets from the openbmb/UltraFeedback and snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset datasets.
Model Inputs and Outputs
Llama-3-Instruct-8B-SPPO-Iter3 is a text-to-text model, meaning it takes in text-based inputs and generates text-based outputs. The model can handle a variety of natural language tasks, including question answering, summarization, and language generation.
Inputs
- Natural language text
- Instructions or prompts for the model to follow
Outputs
- Generated text responses
- Answers to questions
- Summaries of input text
Capabilities
Llama-3-Instruct-8B-SPPO-Iter3 has demonstrated strong performance on a range of language tasks, as shown by its high scores on the AlpacaEval and Open LLM Leaderboard benchmarks. The model is particularly capable at tasks that require reasoning, inference, and coherent text generation.
What Can I Use It For?
Llama-3-Instruct-8B-SPPO-Iter3 can be used for a variety of natural language processing applications, such as:
- Chatbots and virtual assistants
- Content generation (e.g., articles, stories, scripts)
- Question answering
- Summarization
- Translation
The model's strong performance on benchmarks suggests it could be a valuable tool for researchers, developers, and businesses working on language-based AI projects.
Things to Try
One interesting aspect of Llama-3-Instruct-8B-SPPO-Iter3 is its ability to generate coherent and contextually-appropriate text. You could try giving the model a variety of prompts and observe the diversity and quality of the responses. Additionally, you could experiment with fine-tuning the model on your own datasets to see how it performs on specific tasks or domains.
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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