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---
tags:
- merge
license: other
model-index:
- name: QuartetAnemoi-70B-t0.0001
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: 73.38
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/QuartetAnemoi-70B-t0.0001
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: 88.9
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/QuartetAnemoi-70B-t0.0001
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: 75.42
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/QuartetAnemoi-70B-t0.0001
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: 69.53
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/QuartetAnemoi-70B-t0.0001
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: 85.32
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/QuartetAnemoi-70B-t0.0001
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: 68.61
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/QuartetAnemoi-70B-t0.0001
name: Open LLM Leaderboard
---
<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)
<br/>
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.
<br/>
<br/>
# Quants
Most of the popular quant formats are available now, thanks to community efforts.
| Type | Misc | Author |
| ----- | ----- | ----- |
| [GGUF](https://huggingface.co/alchemonaut/QuartetAnemoi-70B-t0.0001-GGUF/tree/main) | | alchemonaut |
| [GGUF](https://huggingface.co/Nexesenex/alchemonaut_QuartetAnemoi-70B-iMat.GGUF) | iMat | Nexesenex |
| [GGUF](https://huggingface.co/mradermacher/QuartetAnemoi-70B-t0.0001-i1-GGUF) | iMat | mradermacher |
| [GGUF](https://huggingface.co/mradermacher/QuartetAnemoi-70B-t0.0001-GGUF) | Full Set | mradermacher |
| [exl2](https://huggingface.co/llmixer/QuartetAnemoi-70B-t0.0001-2.5bpw-h6-exl2) | 2.5bpw | llmixer |
| [exl2](https://huggingface.co/altomek/QuartetAnemoi-70B-t0.0001-3.75bpw-EXL2) | 3.75bpw | altomek |
| [exl2](https://huggingface.co/llmixer/QuartetAnemoi-70B-t0.0001-4bpw-h6-exl2) | 4.0bpw| llmixer |
| [exl2](https://huggingface.co/alchemonaut/QuartetAnemoi-70B-t0.0001-b4.6-h8-exl2) | 4.6bpw| alchemonaut |
| [exl2](https://huggingface.co/llmixer/QuartetAnemoi-70B-t0.0001-6.0bpw-h6-exl2) | 6.0bpw | llmixer |
| [AWQ](https://huggingface.co/tachyphylaxis/QuartetAnemoi-70B-t0.0001-AWQ) | | tachyphylaxis |
<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/>
# [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|
|