Upload HfMoondream
Browse files- config.py +1 -1
- layers.py +9 -2
- model.safetensors +2 -2
config.py
CHANGED
@@ -38,7 +38,7 @@ class RegionConfig:
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size_feat_dim: int = 512
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size_out_dim: int = 2048
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inner_dim: int = 8192
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group_size: Optional[int] =
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@dataclass(frozen=True)
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class TokenizerConfig:
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size_feat_dim: int = 512
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size_out_dim: int = 2048
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inner_dim: int = 8192
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group_size: Optional[int] = None
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@dataclass(frozen=True)
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class TokenizerConfig:
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layers.py
CHANGED
@@ -7,8 +7,6 @@ from typing import Literal
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from torchao import quantize_
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from torchao.quantization import int4_weight_only
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from .packing import dequantize_tensor
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def gelu_approx(x):
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return F.gelu(x, approximate="tanh")
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@@ -24,6 +22,15 @@ def linear(x: torch.Tensor, w: LinearWeights) -> torch.Tensor:
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return F.linear(x, w.weight, w.bias)
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class QuantizedLinear(nn.Module):
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def __init__(
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self,
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from torchao import quantize_
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from torchao.quantization import int4_weight_only
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def gelu_approx(x):
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return F.gelu(x, approximate="tanh")
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return F.linear(x, w.weight, w.bias)
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def dequantize_tensor(W_q, scale, zero, orig_shape, dtype=torch.bfloat16):
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_step = W_q.shape[0]
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W_r = torch.empty([2 * _step, W_q.shape[1]], dtype=dtype, device=W_q.device)
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W_r[:_step] = (W_q & 0b11110000) >> 4
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W_r[_step:] = W_q & 0b00001111
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W_r.sub_(zero).mul_(scale)
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return W_r.reshape(orig_shape)
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class QuantizedLinear(nn.Module):
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def __init__(
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self,
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model.safetensors
CHANGED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:de670ae0501f95abc4b15669d80da1551cb5ec66ce80bbae0fb290c212d5eb95
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size 2119003880
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