mistral-ft-optimized-1227
Maintainer: OpenPipe
77
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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 mistral-ft-optimized-1227 model is an AI model developed by OpenPipe. It is a hierarchical SLERP merge of several base models, including teknium/OpenHermes-2.5-Mistral-7B, Intel/neural-chat-7b-v3-3, meta-math/MetaMath-Mistral-7B, and openchat/openchat-3.5-1210. This model is intended to be a strong base suitable for downstream fine-tuning on a variety of tasks, and according to the maintainer's internal evaluations, it is one of the strongest models for most downstream tasks.
The similar mistral-ft-optimized-1218 model was previously released, but the maintainer recommends the updated mistral-ft-optimized-1227 model as it has similar performance and a more permissive license.
Model inputs and outputs
Inputs
- Text: The model can take in text-based inputs for a variety of tasks.
Outputs
- Text: The model generates text-based outputs, which can include responses to questions, generated content, and more.
Capabilities
The mistral-ft-optimized-1227 model demonstrates strong performance across a range of benchmarks, including GPT4All, AGIEval, BigBench, and TruthfulQA. It outperforms previous versions of the Mistral model as well as many other current Mistral fine-tunes.
What can I use it for?
The mistral-ft-optimized-1227 model can be used for a variety of text-based tasks, such as question answering, content generation, and language understanding. The maintainer's description suggests it is particularly well-suited for downstream fine-tuning on a variety of tasks, making it a versatile base model for further development.
Things to try
One interesting aspect of the mistral-ft-optimized-1227 model is its ability to perform well on both code-related and non-code-related tasks. The maintainer's description notes that training the model on a good ratio of code datasets has boosted its performance on several non-code benchmarks, including TruthfulQA, AGIEval, and GPT4All. This suggests the model has developed a strong general-purpose language understanding capability that could be leveraged for a wide range of applications.
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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