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--- |
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tags: |
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- merge |
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license: other |
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--- |
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<img src=https://huggingface.co/alchemonaut/QuartetAnemoi-70B-t0.0001/resolve/main/anemoi.png> |
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# QuartetAnemoi-70B-t0.0001 |
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A sequential merge using a custom algorithm (NearSwap) of: |
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- [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) |
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- [Sao10K/WinterGoddess-1.4x-70B-L2](https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2) |
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- [Aurora-Nights-70B-v1.0](https://huggingface.co/sophosympatheia/Aurora-Nights-70B-v1.0) |
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- [Xwin-LM-70B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1) |
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In our testing, this model seems like a storyteller, as might be expected. We were impressed that, unlike most models, at the end of a story it did not often use cliches such as "In the end", "And so", "beacon of hope", etc. |
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# NearSwap Algorithm |
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NearSwap retains most of the weights of the base model (Miqu), but when a weight is similar between the two, it is interpolated to the secondary model value. A parameter *t* specifies the sameness threshold. When the distance between two values is below *t*, the weight from the secondary model is used. |
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This version of the model uses *t* = 0.0001. At this *t*, about 0.8% of weights are fully switched to the secondary model during each pass. Model quality rapidly degrades above *t* = 0.0025: |
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- *t* = 0.0001 (~0.8% full swap): This model |
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- *t* = 0.0003 (~2% full swap) |
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- *t* = 0.001 (~10% full swap): [BoreanGale-70B](https://huggingface.co/alchemonaut/BoreanGale-70B) |
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- *t* = 0.0025 (~18% full swap): Generates one paragraph okay, but then reverts to garbage |
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- *t* = 0.005 (~35% full swap): Garbage; semi-related word lists |
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- *t* = 0.01 (~55% full swap): Garbage; pseudorandom tokens output |
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For QuartetAnemoi-70B-t0.0001, the three secondary models were each merged sequentially with *t* = 0.0001. |
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NearSwap implementation: |
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``` |
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t: Union[float, np.ndarray], |
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v0: Union[np.ndarray, torch.Tensor], |
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v1: Union[np.ndarray, torch.Tensor], |
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... |
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lweight = numpy.absolute(v0-v1) |
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lweight = t / lweight |
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lweight = numpy.nan_to_num(lweight, nan=1.0, posinf=1.0, neginf=1.0) |
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numpy.clip(lweight, a_min=0.0, a_max=1.0, out=lweight) |
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res = lerp(lweight,v0,v1) |
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``` |
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# License and Use |
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Since the ultimate origin of Miqu is at this time unknown beyond speculation, this model is for noncommercial research use only. |
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