---
license: mit
tags:
- phi3
- nlp
- moe
datasets:
- BEE-spoke-data/gutenberg-en-v1-clean
- NeelNanda/pile-10k
---
# phi 3 4x4b
a continually pretrained phi3-mini sparse moe upcycle

## benchmarks
### ran locally

|                         | Microsoft/phi-3-4k-instruct | Fizzarolli/phi3-4x4b-v1 |
| ----------------------- | --------------------------- | ----------------------- |
| MMLU acc. (0-shot)      | **0.6799**                  | 0.6781                  |
| Hellaswag acc. (0-shot) | **0.6053**                  | 0.5962                  |
| ARC-E acc. (0-shot)     | 0.8325                      | **0.8367**              |
| ARC-C acc. (0-shot)     | 0.5546                      | **0.5606**              |

honestly i was expecting it to do worse :p, but those are all within a margin of error! so it didn't *lose* any performance, at least

### open llm leaderboard
todo!

## support me on ko-fi!
[~~please i need money to stay alive and keep making models~~](https://ko-fi.com/fizzai)

## notes
*not trained on instruct data.* it's pretty likely that it won't be much different from phi 3 if you use it like that, if not worse due to any forgetting of instruct formats during the continued training.

## future experiments
- the datasets for this were literally chosen on a whim. perhaps experiment with a further filtered [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)?
- actually freeze the gate layers next time (see [Chen et. al, 2023](https://arxiv.org/abs/2303.01610)), oops
- MOAR TRAINING, this only went up to ~0.2 of an epoch because i ran out of dolar