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--- |
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license: mit |
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tags: |
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- phi3 |
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- nlp |
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- moe |
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datasets: |
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- BEE-spoke-data/gutenberg-en-v1-clean |
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- NeelNanda/pile-10k |
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--- |
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# phi 3 4x4b |
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a continually pretrained phi3-mini sparse moe upcycle |
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## benchmarks |
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### ran locally |
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| | Microsoft/phi-3-4k-instruct | Fizzarolli/phi3-4x4b-v1 | |
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| ----------------------- | --------------------------- | ----------------------- | |
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| MMLU acc. (0-shot) | **0.6799** | 0.6781 | |
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| Hellaswag acc. (0-shot) | **0.6053** | 0.5962 | |
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| ARC-E acc. (0-shot) | 0.8325 | **0.8367** | |
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| ARC-C acc. (0-shot) | 0.5546 | **0.5606** | |
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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 |
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### open llm leaderboard |
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todo! |
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## support me on ko-fi! |
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[~~please i need money to stay alive and keep making models~~](https://ko-fi.com/fizzai) |
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## notes |
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*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. |
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## future experiments |
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- 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)? |
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- actually freeze the gate layers next time (see [Chen et. al, 2023](https://arxiv.org/abs/2303.01610)), oops |
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- MOAR TRAINING, this only went up to ~0.2 of an epoch because i ran out of dolar |
