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Locutusque/Hyperion-3.0-Mixtral-3x7B
Hyperion-3.0-Mixtral-3x7B is a text generation model from Locutusque. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
This is an experimental first attempt at creating a Mixture of Experts (MoE) language model by combining several Mistral expert models. The model uses the hyperion-3.0-beta architecture as the base, with a bfloat16 ou…
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From the Hugging Face model README
This is an experimental first attempt at creating a Mixture of Experts (MoE) language model by combining several Mistral expert models. The model uses the hyperion-3.0-beta architecture as the base, with a bfloat16 output dtype. The gating mechanism is set to hidden and two experts are consulted per token (experts_per_token: 2).
The model incorporates three expert models:
hyperion-3.0-beta: Focused on science, math, and coding tasksdibt-mistral-7b: Handles open-ended questions, summarization, and stream of consciousness.rp-mistral-7b: Specializes in roleplaying and character-based conversationsEach expert is trained on a set of positive and negative prompts to guide its specialization.
This MoE model is an early prototype and may not exhibit optimal performance. It is intended for research and experimentation purposes only, and should not be used in production environments or for critical applications.
Please note that the expert models mentioned in the configuration have not been publicly released yet. They are expected to be made available in the near future, at which point this MoE model can be fully instantiated and evaluated.
The base model and experts were trained using QLoRA and SFT. However, the specific details of the training data, hyperparameters, and optimization techniques used for this MoE model are not available at this time.
As this is an experimental model, feedback and suggestions are welcome. Future updates may include improvements to the gating mechanism, fine-tuning of the expert models, and the incorporation of additional experts to enhance the model's performance and breadth of knowledge.