---
tags:
- merge
license: other
model-index:
- name: BoreanGale-70B
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 73.89
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/BoreanGale-70B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 89.37
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/BoreanGale-70B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 75.19
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/BoreanGale-70B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 68.6
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/BoreanGale-70B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 84.53
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/BoreanGale-70B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 67.32
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/BoreanGale-70B
      name: Open LLM Leaderboard
---

<img src=https://huggingface.co/alchemonaut/BoreanGale-70B/resolve/main/bg.png>

# BoreanGale-70B

A 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)

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# Quants
Several quants are available thanks to community efforts.

| Type  | Misc   | Author        |
| ----- | -----  | -----         |
| [GGUF](https://huggingface.co/Nexesenex/alchemonaut_BoreanGale-70B-iMat.GGUF)  | iMat Q3  | Nexesenex      |
| [GGUF](https://huggingface.co/mradermacher/BoreanGale-70B-i1-GGUF)  | iMat   | mradermacher     |
| [GGUF](https://huggingface.co/mradermacher/BoreanGale-70B-GGUF)  | Q8_0 | mradermacher     |


# 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 (WinterGoddess) value. A parameter *t* specifies the sameness threshold. When the distance between two values is below *t*, the weight from the secondary model (WinterGoddess) is used.

This version of the model uses *t* = 0.001. At this *t*, about 10% of weights are fully switched to WinterGoddess. Model quality rapidly degrades above *t* = 0.0025:

- *t* = 0.0001 (~0.8% full swap): [QuartetAnemoi-70B-t0.0001](https://huggingface.co/alchemonaut/QuartetAnemoi-70B-t0.0001)
- *t* = 0.0003 (~2%  full swap)
- *t* = 0.001  (~10% full swap): This model
- *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

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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<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.

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<br/>

# [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__BoreanGale-70B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |76.48|
|AI2 Reasoning Challenge (25-Shot)|73.89|
|HellaSwag (10-Shot)              |89.37|
|MMLU (5-Shot)                    |75.19|
|TruthfulQA (0-shot)              |68.6|
|Winogrande (5-shot)              |84.53|
|GSM8k (5-shot)                   |67.32|