Update README.md

README.md

@@ -20,6 +20,6 @@ QLoRA tuned from [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/
 
 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](https://arxiv.org/abs/2202.08906). 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](https://github.com/stanford-futuredata/megablocks).
+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](https://github.com/stanford-futuredata/megablocks). The config used with my custom hacked-up branch of axolotl is available [here](https://huggingface.co/chargoddard/MixtralRPChat-ZLoss/blob/main/axolotl_config.yml).
 
-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.
+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.