Ucla-agi
Models by this creator
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Gemma-2-9B-It-SPPO-Iter3
92
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.
Updated 8/7/2024
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Llama-3-Instruct-8B-SPPO-Iter3
71
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.
Updated 7/31/2024
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Llama-3-Instruct-8B-SPPO-Iter3
71
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.
Updated 7/31/2024