dbrx-instruct-4bit
Maintainer: mlx-community
48
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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 dbrx-instruct-4bit model is a text-to-text AI model created by the mlx-community. It was converted from the original databricks/dbrx-instruct model using the mlx-lm tool. This model is a Mixture-of-Experts (MoE) large language model trained by Databricks, and is an instruction-following variant of their base dbrx-base model. Compared to similar MoE models like Meta-Llama-3-8B-Instruct-4bit and Mixtral-8x22B-4bit, the dbrx-instruct-4bit model uses a fine-grained MoE architecture with more, smaller experts to improve quality.
Model inputs and outputs
The dbrx-instruct-4bit model is a text-to-text model, meaning it takes text-based inputs and produces text-based outputs. It can accept context lengths up to 32,768 tokens.
Inputs
- Text-based prompts and instructions
Outputs
- Text-based responses and completions
Capabilities
The dbrx-instruct-4bit model has been fine-tuned on a large, diverse dataset to specialize in few-turn interactions and instruction-following tasks. It demonstrates strong performance on a wide range of language understanding, reasoning, and problem-solving benchmarks.
What can I use it for?
The dbrx-instruct-4bit model is a general-purpose, open-source language model that can be used for a variety of natural language processing tasks. Some potential use cases include:
- Building conversational AI assistants that can follow instructions and engage in task-oriented dialogs
- Generating human-like text for creative writing, content creation, or dialogue systems
- Providing question-answering capabilities for research or educational applications
- Aiding in code generation, explanation, and other programming-related tasks
Things to try
One interesting aspect of the dbrx-instruct-4bit model is its fine-grained MoE architecture, which allows it to flexibly combine a large number of smaller experts to improve performance. You could experiment with providing the model with diverse prompts and instructions to see how it leverages this capability. Additionally, the model's strong performance on benchmarks like the Databricks Model Gauntlet suggests it may be useful for a wide range of language understanding and reasoning tasks.
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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