NeuralBeagle14-7B-GGUF
Maintainer: mlabonne
45
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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
NeuralBeagle14-7B is a 7B parameter language model that was fine-tuned from mlabonne/Beagle14-7B using the argilla/distilabel-intel-orca-dpo-pairs preference dataset and a direct preference optimization (DPO) training process. According to the maintainer, this model displays good performance in instruction following and reasoning tasks, and can also be used for role-playing and storytelling. Compared to other 7B models, NeuralBeagle14-7B is considered one of the best-performing models in this size range.
Model inputs and outputs
NeuralBeagle14-7B is a text-to-text language model, meaning it takes text as input and generates text as output. It uses a context window of 8,192 tokens and is compatible with different templates, like chatml and Llama's chat template.
Inputs
- Text prompts for the model to generate a response to
Outputs
- Coherent and contextually relevant text generated by the model, based on the input prompt
Capabilities
NeuralBeagle14-7B displays strong performance on a variety of benchmarks, including instruction following, reasoning, and truthfulness tasks. According to the evaluation results, it outperforms other 7B models like mlabonne/Beagle14-7B, mlabonne/NeuralDaredevil-7B, and argilla/distilabeled-Marcoro14-7B-slerp.
What can I use it for?
NeuralBeagle14-7B can be used for a variety of natural language processing tasks, including:
- Conversational AI and chatbots
- Assistants for task completion and information retrieval
- Creative writing and storytelling
- Role-playing and interactive narratives
The model's strong performance on reasoning and truthfulness tasks also makes it potentially useful for educational applications and decision support systems.
Things to try
One interesting thing to try with NeuralBeagle14-7B is exploring how it handles more open-ended and creative prompts, such as world-building exercises or collaborative storytelling. Its ability to reason and follow instructions may lend itself well to these types of tasks, allowing for engaging and imaginative interactions.
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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