Open-Orca/Mistral-7B-OpenOrca · Hugging Face
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This release is trained on a curated filtered subset of most of our GPT-4 augmented data.

HF Leaderboard evals place this model as #2 for all models smaller than 30B at release time, outperforming all but one 13B model.

GGUF files:

Mistral-7B-OpenOrca-GGUF

Warning (if I’m not mistaken):

Llama.cpp hasn’t assigned high priority tag to the sliding window. Axolotl replaced Mistral’s attention block by a “simple” flash attention.

That implies, in my opinion, that the new releases do not capitalize on the speedup claimed by Mistral developers.

We can’t expect the new versions to be faster than Llama, because there is no sliding attention to speed up inference.

  • noneabove1182@sh.itjust.worksMEnglish
    4·
    1 year ago

    I LOVE orca tunes, they almost always end up feeling like smarter versions of the base, so i’m looking forward to trying this one out when the GPTQ is finished

    GPTQ/AWQ links:

    https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-GPTQ

    https://huggingface.co/TheBloke/Mistral-7B-OpenOrca-AWQ

    Does sliding attention speed up inference? I thought it was more about extending the capabilities of the context above what it was trained on. I suppose I could see it being used to drop context which would save on memory/inference, but didn’t think that was the point of it, just a happy side effect, i could be wrong though

    • justynasty@lemmy.kya.moeOPEnglish
      5·
      1 year ago

      Mistral 7B uses a sliding window attention (SWA) mechanism (Child et al., Beltagy et al.), in which each layer attends to the previous 4,096 hidden states. The main improvement, and reason for which this was initially investigated, is a linear compute cost of O(sliding_window.seq_len). In practice, changes made to FlashAttention and xFormers yield a 2x speed improvement for sequence length of 16k with a window of 4k. Source: Mistral 7B news For longer prompts.

      Talk about merging changes