[REMOVED SCIPY]

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "zetascale"
-version = "2.5.2"
+version = "2.5.4"
 description = "Rapidly Build, Optimize, and Deploy SOTA AI Models"
 authors = ["Zeta Team <[email protected]>"]
 license = "MIT"
@@ -21,15 +21,14 @@ torch = ">=2.1.1,<3.0"
 pytest = "8.2.1"
 torchfix = "*"
 einops = "0.7.0"
-bitsandbytes = "0.43.0"
+bitsandbytes = "*"
 transformers = "4.41.0"
 einops-exts = "0.0.4"
 torchvision = "0.18.0"
 accelerate = "0.30.1"
 datasets = "*"
 loguru = "*"
 vector-quantize-pytorch = "1.14.7"
-scipy = "1.9.3"
 beartype = "0.17.2"
 tqdm = "4.66.3"
 rich = "13.7.1"

requirements.txt

@@ -11,7 +11,6 @@ torchfix
 torchdiffeq>=0.2.3,<0.3.0
 beartype>=0.15.0,<0.16.0
 vector-quantize-pytorch>=1.12.0,<1.13.0
-scipy>=1.9.3,<1.10.0
 loguru
 rich==13.7.1
 tiktoken==0.6.0

zeta/quant/qlora.py

@@ -4,7 +4,7 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from scipy.stats import norm
+# from scipy.stats import norm
 from tqdm import tqdm
 
 bnb_available = False
@@ -362,9 +362,9 @@ def get_nf4(cached=True) -> torch.Tensor:
  )
 
  offset = 0.9677083
- v1 = norm.ppf(torch.linspace(offset, 0.5, 9)[:-1]).tolist()
+ v1 = torch.linspace(offset, 0.5, 9)[:-1].tolist()
  # v2 = [0]*(256-15)
- v3 = (-norm.ppf(torch.linspace(offset, 0.5, 8)[:-1])).tolist()
+ v3 = (torch.linspace(offset, 0.5, 8)[:-1]).tolist()
  # v = v1 + v3 + 0.0
  nkf = torch.tensor(v1 + v3 + [0.0])
  nkf = nkf.sort().values