mixtralnt-4x7b-test
Maintainer: chargoddard
56
🗣️
| 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 mixtralnt-4x7b-test model is an experimental AI model created by the maintainer chargoddard. It is a Sparse Mixture of Experts (MoE) model that combines parts from several pre-trained Mistral models, including Q-bert/MetaMath-Cybertron-Starling, NeverSleep/Noromaid-7b-v0.1.1, teknium/Mistral-Trismegistus-7B, meta-math/MetaMath-Mistral-7B, and PocketDoc/Dans-AdventurousWinds-Mk2-7b. The maintainer is experimenting with a hack to populate the MoE gates in order to take advantage of the experts.
Model inputs and outputs
The mixtralnt-4x7b-test model is a text-to-text model, meaning it takes text as input and generates text as output. The specific input and output formats are not clearly defined, but the maintainer suggests the model may use an "alpaca??? or chatml??? format".
Inputs
- Text prompts in an unspecified format, potentially related to alpaca or chatml
Outputs
- Generated text in response to the input prompts
Capabilities
The mixtralnt-4x7b-test model is capable of generating coherent text, taking advantage of the experts from the combined Mistral models. However, the maintainer is still experimenting with the hack used to populate the MoE gates, so the full capabilities of the model are not yet known.
What can I use it for?
The mixtralnt-4x7b-test model could potentially be used for a variety of text generation tasks, such as creative writing, conversational responses, or other applications that require generating coherent text. However, since the model is still in an experimental stage, it's unclear how it would perform compared to more established language models.
Things to try
One interesting aspect of the mixtralnt-4x7b-test model is the maintainer's approach of combining parts of several pre-trained Mistral models into a Sparse Mixture of Experts. This technique could lead to improvements in the model's performance and capabilities, but the results are still unknown. It would be worth exploring the model's output quality, coherence, and consistency to see how it compares to other language models.
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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