<img src=https://huggingface.co/alchemonaut/BoreanGale-70B/resolve/main/bg.png>
BoreanGale-70B
A merge using a custom algorithm (NearSwap) of:
Quants
Several quants are available thanks to community efforts.
| Type | Misc | Author |
|---|---|---|
| GGUF | iMat Q3 | Nexesenex |
| GGUF | iMat | mradermacher |
| GGUF | Full Set | mradermacher |
| GGUF | Misc | LoneStriker |
| exl2 | 2.4 bpw | LoneStriker |
| exl2 | 3.5 bpw | LoneStriker |
| exl2 | 4.0 bpw | LoneStriker |
| exl2 | 4.65 bpw | LoneStriker |
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 |