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
BoreanGale-70B
A merge using a custom algorithm (NearSwap) of:
Quants
Several quants are available thanks to community efforts.
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
- 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)
License and Use
Since the ultimate origin of Miqu is at this time unknown beyond speculation, this model is for noncommercial research use only.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| 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 |