
<img src=https://huggingface.co/alchemonaut/QuartetAnemoi-70B-t0.0001/resolve/main/anemoi.png>

# 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)

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In our testing, this model seems like a storyteller, as might be expected, but the changes from this merge are extremely soft. 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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GGUF quants available at: [alchemonaut/QuartetAnemoi-70B-t0.0001-GGUF](https://huggingface.co/alchemonaut/QuartetAnemoi-70B-t0.0001-GGUF/tree/main). <br/>

Some exl2 quants were made by llmixer too:
 - [2.5bpw](https://huggingface.co/llmixer/QuartetAnemoi-70B-t0.0001-2.5bpw-h6-exl2)
 - [4.0bpw](https://huggingface.co/llmixer/QuartetAnemoi-70B-t0.0001-4bpw-h6-exl2)
 - [6.0bpw](https://huggingface.co/llmixer/QuartetAnemoi-70B-t0.0001-6.0bpw-h6-exl2)

As well as [GGUF iMatrix quants](https://huggingface.co/Nexesenex/alchemonaut_QuartetAnemoi-70B-iMat.GGUF) by Nexesenex.

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# 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)
```
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# License and Use

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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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_alchemonaut__QuartetAnemoi-70B-t0.0001)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |76.86|
|AI2 Reasoning Challenge (25-Shot)|73.38|
|HellaSwag (10-Shot)              |88.9|
|MMLU (5-Shot)                    |75.42|
|TruthfulQA (0-shot)              |69.53|
|Winogrande (5-shot)              |85.32|
|GSM8k (5-shot)                   |68.61|


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transformers safetensors llama text-generation merge license:other model-index autotrain_compatible endpoints_compatible text-generation-inference region:us