DiscoLM-mixtral-8x7b-v2
Maintainer: DiscoResearch
122
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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 DiscoLM Mixtral 8x7b alpha is an experimental 8x7b Mixture-of-Experts model based on Mistral AI's Mixtral 8x7b. The model was created by Bjrn Plster with the DiscoResearch team and has been fine-tuned on the Synthia, MethaMathQA and Capybara datasets. Compared to similar models like Mixtral-8x7B-v0.1 and Mixtral-8x7B-Instruct-v0.1, the DiscoLM Mixtral 8x7b alpha incorporates additional fine-tuning and updates.
Model inputs and outputs
The DiscoLM Mixtral 8x7b alpha is a large language model that can generate human-like text based on given prompts. It takes in natural language text as input and produces coherent, contextually relevant text as output.
Inputs
- Natural language prompts or text
Outputs
- Continuation of the input text, generating new coherent text
- Responses to questions or instructions based on the input
Capabilities
The DiscoLM Mixtral 8x7b alpha demonstrates strong performance on a variety of benchmarks, including the ARC (25-shot), HellaSwag (10-shot), MMLU (5-shot), TruthfulQA (0-shot), and Winogrande (5-shot) tasks. Its diverse capabilities make it suitable for open-ended text generation, question answering, and other language-based applications.
What can I use it for?
The DiscoLM Mixtral 8x7b alpha can be used for a wide range of natural language processing tasks, such as:
- Generating creative fiction or poetry
- Summarizing long-form text
- Answering questions and providing information
- Assisting with research and analysis
- Improving language learning and education
- Enhancing chatbots and virtual assistants
DiscoResearch and the maintainer have made this model available to the community, enabling developers and researchers to explore its potential applications.
Things to try
One interesting aspect of the DiscoLM Mixtral 8x7b alpha is its potential for generating diverse and imaginative text. Experiment with providing the model with open-ended prompts or creative writing exercises to see how it can expand on and develop new ideas. Additionally, you can leverage the model's question-answering capabilities by posing informational queries and evaluating the coherence and accuracy of its 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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