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metadata
license: cc-by-nc-4.0
datasets:
  - Open-Orca/SlimOrca
  - lemonilia/LimaRP
  - chargoddard/rpguild
  - chargoddard/summarize_from_feedback_alpaca
  - HuggingFaceH4/no_robots
  - chargoddard/coedit-reworded
language:
  - en
tags:
  - mixtral
base_model: mistralai/Mixtral-8x7B-v0.1

Built with Axolotl

QLoRA tuned from mistralai/Mixtral-8x7B-v0.1.

My main reason for training this model was to investigate using an altered router balancing loss combined with the z-loss introduced in ST-MoE: Designing Stable and Transferable Sparse Expert Models. The result is pretty decent, I think! It does a good job of respecting character information in system prompts and performed adequately on a few simple coding tasks.

To train this I used a custom branch of Transformers that adds z-loss and reimplements the router balancing loss based on the version in MegaBlocks. The config used with my custom hacked-up branch of axolotl is available here.

Uses my favorite non-ChatML token-economic chat prompt format. Messages should be prefixed with " ***System:", " ***Query:", or " ***Response:" for system, user, and model messages respectively. No newlines are necessary but the space before the triple asterisk is mandatory.