Snorkelai

Models by this creator

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Snorkel-Mistral-PairRM-DPO

snorkelai

Total Score

102

The Snorkel-Mistral-PairRM-DPO model is a large language model (LLM) developed by the team at Snorkel AI. It is based on the Mistral-7B-Instruct-v0.2 model and has been further optimized using a process called Direct Preference Optimization (DPO). The model was trained on a dataset from the UltraFeedback dataset, with the goal of improving its performance on chat-related tasks. The model can be tried out on the Together AI playground, and an HuggingFace inference endpoint is also available for initial testing. The model is also available through the Together AI API with the API string snorkelai/Snorkel-Mistral-PairRM-DPO. Model inputs and outputs Inputs Prompts**: The model takes in natural language prompts as input, following the Mistral model format: [INST] {prompt} [/INST]. Outputs Text responses**: The model generates natural language text responses to the provided prompts. Capabilities The Snorkel-Mistral-PairRM-DPO model is optimized for chat-related tasks, such as providing recommendations, answering questions, and engaging in open-ended conversations. The model's training process, which involved generating multiple response variations and then reranking and improving them using DPO, has resulted in more coherent and contextually appropriate outputs compared to the original Mistral-7B-Instruct-v0.2 model. What can I use it for? The Snorkel-Mistral-PairRM-DPO model can be useful for a variety of chat-based applications, such as virtual assistants, customer service chatbots, and conversational AI interfaces. Its ability to generate relevant and coherent responses makes it well-suited for tasks like providing recommendations, answering questions, and engaging in open-ended discussions. Things to try One interesting thing to try with the Snorkel-Mistral-PairRM-DPO model is to provide prompts that require the model to engage in more complex reasoning or multi-turn interactions. The model's training process, which involved optimizing for coherence and contextual appropriateness, may enable it to handle such prompts more effectively than a standard LLM. Developers could also experiment with fine-tuning the model further on specialized datasets or tasks to enhance its capabilities in specific domains.

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Updated 5/17/2024