mixtral-7b-8expert
Maintainer: DiscoResearch
258
🗣️
| 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 mixtral-7b-8expert is a preliminary HuggingFace implementation of a newly released Mixture of Experts (MoE) model by MistralAi. The model is capable of Text-to-Text tasks and was created by the DiscoResearch team. It is based on an early implementation by Dmytro Dzhulgakov that helped find a working setup. The model was trained with compute provided by LAION and HessianAI.
Similar models include the DiscoLM-mixtral-8x7b-v2, Mixtral-8x7B-v0.1, Mixtral-8x7B-Instruct-v0.1, and Mixtral-8x22B-v0.1 models, all of which are based on the Mixtral MoE architecture.
Model inputs and outputs
The mixtral-7b-8expert model takes text prompts as input and generates text responses. The model can be used for a variety of natural language processing tasks such as text generation, summarization, and question answering.
Inputs
- Text prompts or conversations
Outputs
- Generated text responses
Capabilities
The mixtral-7b-8expert model is capable of generating coherent and contextually relevant text responses. It has been benchmarked on a range of tasks including HellaSwag, TruthfulQA, and MMLU, demonstrating strong performance compared to other large language models.
What can I use it for?
The mixtral-7b-8expert model can be used for a variety of applications that require natural language generation, such as chatbots, content creation tools, and language learning assistants. Its ability to generate high-quality text makes it a useful tool for tasks like story writing, article generation, and dialogue systems.
Things to try
One interesting aspect of the mixtral-7b-8expert model is its Mixture of Experts architecture, which allows it to leverage multiple specialized sub-models to generate more diverse and nuanced outputs. Experimenting with different prompts and prompt engineering techniques may reveal interesting capabilities or biases in the model's knowledge and reasoning.
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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