notus-7b-v1
Maintainer: argilla
113
🏷️
| 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
notus-7b-v1 is a 7B parameter language model fine-tuned by Argilla using Direct Preference Optimization (DPO) on a curated version of the UltraFeedback dataset. This model was developed as part of the Notus family of models, which explore data-first and preference tuning methods. Compared to the similar zephyr-7b-beta model, notus-7b-v1 uses a modified preference dataset that led to improved performance on benchmarks like AlpacaEval.
Model inputs and outputs
Inputs
- Text prompts for the model to continue or generate.
Outputs
- Continuation of the input text, generating coherent and contextually relevant responses.
Capabilities
notus-7b-v1 demonstrates strong performance on chat-based tasks as evaluated on the MT-Bench and AlpacaEval benchmarks. It surpasses the Zephyr-7b-beta and Claude 2 models in these areas. However, the model has not been fully aligned for safety, so it may produce problematic outputs when prompted to do so.
What can I use it for?
Argilla intends for notus-7b-v1 to be used as a helpful assistant in chat-like applications. The model's capabilities make it well-suited for tasks like open-ended conversation, question answering, and task completion. However, users should be cautious when interacting with the model, as it lacks the safety alignment of more constrained models like ChatGPT.
Things to try
Explore the model's capabilities in open-ended conversations and task-oriented prompts. Pay attention to the model's reasoning abilities and its tendency to provide relevant and contextual responses. However, be mindful of potential biases or safety issues that may arise, and use the model with appropriate precautions.
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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