Create README.md

README.md

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+---
+tags:
+- merge
+license: other
+---
+
+# QuartetAnemoi-70B-t0.0001
+
+A sequential merge using a custom algorithm (NearSwap) of:
+- [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf)
+- [Sao10K/WinterGoddess-1.4x-70B-L2](https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2)
+- [Aurora-Nights-70B-v1.0](https://huggingface.co/sophosympatheia/Aurora-Nights-70B-v1.0)
+- [Xwin-LM-70B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1)
+
+<br/>
+
+In our testing, this model seems like much more of a storyteller than a coder, 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.
+
+<br/>
+<br/>
+
+# NearSwap Algorithm
+
+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.
+
+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:
+
+- *t* = 0.0001 (~0.8% full swap): This model
+- *t* = 0.0003 (~2%  full swap)
+- *t* = 0.001  (~10% full swap): [BoreanGale-70B](https://huggingface.co/alchemonaut/BoreanGale-70B)
+- *t* = 0.0025 (~18% full swap): Generates one paragraph okay, but then reverts to garbage
+- *t* = 0.005  (~35% full swap): Garbage; semi-related word lists
+- *t* = 0.01   (~55% full swap): Garbage; pseudorandom tokens output
+
+For QuartetAnemoi-70B-t0.0001, the three secondary models were each merged sequentially with *t* = 0.0001.
+
+NearSwap implementation:
+```
+    t: Union[float, np.ndarray],
+    v0: Union[np.ndarray, torch.Tensor],
+    v1: Union[np.ndarray, torch.Tensor],
+...
+    lweight = numpy.absolute(v0-v1)
+    lweight = t / lweight
+    lweight = numpy.nan_to_num(lweight, nan=1.0, posinf=1.0, neginf=1.0)
+    numpy.clip(lweight, a_min=0.0, a_max=1.0, out=lweight)
+    res = lerp(lweight,v0,v1)
+```
+<br/>
+<br/>
+
+
+# License and Use
+
+Since the ultimate origin of Miqu is at this time unknown beyond speculation, this model is for noncommercial research use only.
+
+<br/>
+<br/>