Mixtral-8x22B-4bit
Maintainer: mlx-community
51
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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 Mixtral-8x22B-4bit is a large language model (LLM) developed by the mlx-community team. It was converted from the original Mixtral-8x22B-v0.1 model created by v2ray using the mlx-lm library. The model is a pre-trained generative Sparse Mixture of Experts (SMoE) with around 176 billion parameters, of which 44 billion are active during inference. It has a 65,000 token context window and a 32,000 vocabulary size.
Similar models include the Meta-Llama-3-8B-Instruct-4bit and the Mixtral-8x22B-v0.1 models, both of which share some architectural similarities with the Mixtral-8x22B-4bit.
Model inputs and outputs
Inputs
- Text prompts of varying lengths, typically a few sentences or a short paragraph.
Outputs
- Continuation of the input text, generating new tokens to extend the prompt in a coherent and contextually relevant manner.
Capabilities
The Mixtral-8x22B-4bit model is capable of generating fluent and contextually appropriate text across a wide range of domains, including creative writing, question answering, summarization, and general language understanding tasks. It can be fine-tuned for specific applications or used as a base model for further customization.
What can I use it for?
The Mixtral-8x22B-4bit model can be a powerful tool for a variety of natural language processing applications, such as:
- Content generation: Producing engaging, human-like text for creative writing, journalism, marketing, and other use cases.
- Question answering: Responding to user queries with relevant and informative answers.
- Summarization: Condensing long-form text into concise, informative summaries.
- Dialogue systems: Powering conversational interfaces for chatbots, virtual assistants, and other interactive applications.
Things to try
One interesting aspect of the Mixtral-8x22B-4bit model is its ability to generate diverse and creative text outputs. Try providing the model with open-ended prompts or creative writing exercises and see how it responds. You can also experiment with fine-tuning the model on specific datasets or tasks to adapt it to your particular needs.
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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