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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
Meta-Llama-3-8B-CSQA-NdLinearLoRA.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4a8335c1009d7878c921bba61a919c04427001aa7c1e5ba42ce91782f0c5cc71
3
+ size 32130190008
README.md CHANGED
@@ -1,3 +1,67 @@
1
- ---
2
- license: apache-2.0
3
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ language: en
4
+ ---
5
+
6
+ # NdLinear-LoRA Fine-Tuned Models
7
+
8
+ This repository contains a collection of language models fine-tuned using a custom NdLinear-LoRA architecture. NdLinear-LoRA is a variant of Low-Rank Adaptation (LoRA) that reshapes weight matrices into N-dimensional tensors and applies a factorized linear transformation for parameter-efficient fine-tuning.
9
+
10
+ ## Available Models
11
+
12
+ Below is a list of the fine-tuned models. For best results, it's recommended to host each model in its own repository on the Hugging Face Hub.
13
+
14
+ | Fine-Tuned Model Name | Base Model | Fine-Tuning Dataset |
15
+ | ------------------------------------------------ | -------------------------- | ------------------- |
16
+ | `Meta-Llama-3-8B-CSQA-NdLinearLoRA` | `meta-llama/Llama-3-8B` | `commonsense_qa` |
17
+ | `Meta-Llama-3-8B-Math10K-NdLinearLoRA` | `meta-llama/Llama-3-8B` | `lmms-lab/Math10K` |
18
+ | `Qwen3-1.7B-CSQA-NdLinearLoRA` | `Qwen/Qwen3-1.7B-Base` | `commonsense_qa` |
19
+ | `Qwen3-1.7B-Math10K-NdLinearLoRA` | `Qwen/Qwen3-1.7B-Base` | `lmms-lab/Math10K` |
20
+
21
+ ## How to Use
22
+
23
+ Because these models use a custom architecture, you must pass `trust_remote_code=True` when loading them. This allows the `transformers` library to download and use the `modeling_ndlinear.py` file that should be included in each model's repository.
24
+
25
+ **Dependencies:** Before you start, make sure you have the necessary libraries installed:
26
+ ```bash
27
+ pip install torch transformers safetensors huggingface_hub accelerate
28
+ pip install ndlinear
29
+ ```
30
+
31
+ ### Example Loading Script
32
+ This script will work for any of the models listed above. Just change the `REPO_ID`.
33
+
34
+ ```python
35
+ import torch
36
+ from transformers import AutoModelForCausalLM, AutoTokenizer
37
+
38
+ # --- Example Usage ---
39
+
40
+ # 1. Choose the model you want to use from the table above
41
+ # Replace "YourUsername" with your Hugging Face username or organization.
42
+ REPO_ID = "YourUsername/Qwen3-1.7B-Math10K-NdLinearLoRA"
43
+
44
+ # 2. Load the model and tokenizer
45
+ # `trust_remote_code=True` is required to load the custom architecture.
46
+ print(f"Loading model: {REPO_ID}")
47
+ model = AutoModelForCausalLM.from_pretrained(
48
+ REPO_ID,
49
+ torch_dtype="auto",
50
+ device_map="auto",
51
+ trust_remote_code=True
52
+ )
53
+ tokenizer = AutoTokenizer.from_pretrained(REPO_ID)
54
+ print("Model and tokenizer loaded successfully.")
55
+
56
+
57
+ # 3. Generate text
58
+ # This prompt is geared for a math model. Adjust it for a QA model if needed.
59
+ prompt = "### Instruction:\\nSolve the following math problem: If a train travels at 60 miles per hour, how long does it take to travel 180 miles?\\n\\n### Solution:\\n"
60
+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
61
+
62
+ with torch.no_grad():
63
+ outputs = model.generate(**inputs, max_new_tokens=150, eos_token_id=tokenizer.eos_token_id)
64
+
65
+ print("\\n--- Generated Output ---")
66
+ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
67
+ ```
modeling_ndlinear.py ADDED
@@ -0,0 +1,53 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import torch.nn as nn
3
+ from ndlinear import NdLinear
4
+
5
+ # This file contains the custom building blocks for the NdLinear-LoRA architecture.
6
+ # It should be in the same directory as the model when loading it.
7
+
8
+ def find_factor(n):
9
+ """Finds the most balanced integer factors for n."""
10
+ for i in range(int(n ** 0.5), 0, -1):
11
+ if n % i == 0:
12
+ return (i, n // i)
13
+ return (1, n)
14
+
15
+ class NdLinearLoRA(nn.Module):
16
+ """The NdLinear-LoRA adapter layer."""
17
+ def __init__(self, d_in, d_out, alpha=1.0, dropout=0.0):
18
+ super().__init__()
19
+ self.d_in = d_in
20
+ self.d_out = d_out
21
+ self.in_factors = find_factor(d_in)
22
+ self.out_factors = find_factor(d_out)
23
+ self.adapter = NdLinear(
24
+ input_dims=self.in_factors,
25
+ hidden_size=self.out_factors,
26
+ transform_outer=False,
27
+ bias=False
28
+ )
29
+ self.scaling = alpha
30
+ self.drop = nn.Dropout(dropout)
31
+
32
+ def forward(self, x):
33
+ orig_shape = x.shape
34
+ x = self.drop(x).view(-1, *self.in_factors)
35
+ y = self.adapter(x).view(*orig_shape[:-1], self.d_out)
36
+ return y * self.scaling
37
+
38
+ class LinearWithNdLinearLoRA(nn.Module):
39
+ """A nn.Linear layer wrapped with the NdLinear-LoRA adapter."""
40
+ def __init__(self, base_layer, alpha=1.0, dropout=0.0):
41
+ super().__init__()
42
+ self.base_layer = base_layer
43
+ for param in self.base_layer.parameters():
44
+ param.requires_grad = False
45
+ self.adapter = NdLinearLoRA(
46
+ d_in=self.base_layer.in_features,
47
+ d_out=self.base_layer.out_features,
48
+ alpha=alpha,
49
+ dropout=dropout
50
+ )
51
+
52
+ def forward(self, x):
53
+ return self.base_layer(x) + self.adapter(x)
ndlinear.py ADDED
@@ -0,0 +1,82 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import torch.nn as nn
3
+ import torch.optim as optim
4
+
5
+
6
+ class NdLinear(nn.Module):
7
+ def __init__(self, input_dims: tuple, hidden_size: tuple, transform_outer=True, bias: bool = True):
8
+ """
9
+ NdLinear: A PyTorch layer for projecting tensors into multi-space representations.
10
+
11
+ Unlike conventional embedding layers that map into a single vector space, NdLinear
12
+ transforms tensors across a collection of vector spaces, capturing multivariate structure
13
+ and topical information that standard deep learning architectures typically lose.
14
+
15
+ Args:
16
+ input_dims (tuple): Shape of input tensor (excluding batch dimension).
17
+ hidden_size (tuple): Target hidden dimensions after transformation.
18
+ """
19
+ super(NdLinear, self).__init__()
20
+
21
+ if len(input_dims) != len(hidden_size):
22
+ raise Exception("Input shape and hidden shape do not match.")
23
+
24
+ self.input_dims = input_dims
25
+ self.hidden_size = hidden_size
26
+ self.num_layers = len(input_dims) # Must match since dims are equal
27
+ # self.relu = nn.ReLU() # self.relu is not being used.
28
+ self.transform_outer = transform_outer
29
+ self.bias = bias
30
+
31
+ # Define transformation layers per dimension
32
+ self.align_layers = nn.ModuleList([
33
+ nn.Linear(input_dims[i], hidden_size[i], bias=self.bias) for i in range(self.num_layers)
34
+ ])
35
+
36
+
37
+ def forward(self, X):
38
+ """
39
+ Forward pass to project input tensor into a new multi-space representation.
40
+ - Incrementally transposes, flattens, applies linear layers, and restores shape.
41
+
42
+ Expected Input Shape: [batch_size, *input_dims]
43
+ Output Shape: [batch_size, *hidden_size]
44
+
45
+ Args:
46
+ X (torch.Tensor): Input tensor with shape [batch_size, *input_dims]
47
+
48
+ Returns:
49
+ torch.Tensor: Output tensor with shape [batch_size, *hidden_size]
50
+ """
51
+ num_transforms = self.num_layers # Number of transformations
52
+
53
+ # Define iteration order
54
+ # transform_indices = range(num_transforms) if transform_outer else reversed(range(num_transforms))
55
+
56
+ for i in range(num_transforms):
57
+ if self.transform_outer:
58
+ layer = self.align_layers[i]
59
+ transpose_dim = i + 1
60
+ else:
61
+ layer = self.align_layers[num_transforms - (i+1)]
62
+ transpose_dim = num_transforms - i
63
+
64
+ # Transpose the selected dimension to the last position
65
+ X = torch.transpose(X, transpose_dim, num_transforms).contiguous()
66
+
67
+ # Store original shape before transformation
68
+ X_size = X.shape[:-1]
69
+
70
+ # Flatten everything except the last dimension
71
+ X = X.view(-1, X.shape[-1])
72
+
73
+ # Apply transformation
74
+ X = layer(X)
75
+
76
+ # Reshape back to the original spatial structure (with new embedding dim)
77
+ X = X.view(*X_size, X.shape[-1])
78
+
79
+ # Transpose the dimension back to its original position
80
+ X = torch.transpose(X, transpose_dim, num_transforms).contiguous()
81
+
82
+ return X
ndlinear_lora_config.json ADDED
@@ -0,0 +1,2708 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "model_name": "meta-llama/Meta-Llama-3-8B",
3
+ "lora_r": 1,
4
+ "lora_alpha": 1,
5
+ "target_modules": [
6
+ "q_proj",
7
+ "k_proj",
8
+ "v_proj",
9
+ "o_proj",
10
+ "gate_proj",
11
+ "up_proj",
12
+ "down_proj"
13
+ ],
14
+ "use_factorized": true,
15
+ "wrapped_modules": {
16
+ "model.layers.0.self_attn.q_proj": {
17
+ "in_features": 4096,
18
+ "out_features": 4096,
19
+ "in_factors": [
20
+ 64,
21
+ 64
22
+ ],
23
+ "out_factors": [
24
+ 64,
25
+ 64
26
+ ]
27
+ },
28
+ "model.layers.0.self_attn.k_proj": {
29
+ "in_features": 4096,
30
+ "out_features": 1024,
31
+ "in_factors": [
32
+ 64,
33
+ 64
34
+ ],
35
+ "out_factors": [
36
+ 32,
37
+ 32
38
+ ]
39
+ },
40
+ "model.layers.0.self_attn.v_proj": {
41
+ "in_features": 4096,
42
+ "out_features": 1024,
43
+ "in_factors": [
44
+ 64,
45
+ 64
46
+ ],
47
+ "out_factors": [
48
+ 32,
49
+ 32
50
+ ]
51
+ },
52
+ "model.layers.0.self_attn.o_proj": {
53
+ "in_features": 4096,
54
+ "out_features": 4096,
55
+ "in_factors": [
56
+ 64,
57
+ 64
58
+ ],
59
+ "out_factors": [
60
+ 64,
61
+ 64
62
+ ]
63
+ },
64
+ "model.layers.0.mlp.gate_proj": {
65
+ "in_features": 4096,
66
+ "out_features": 14336,
67
+ "in_factors": [
68
+ 64,
69
+ 64
70
+ ],
71
+ "out_factors": [
72
+ 112,
73
+ 128
74
+ ]
75
+ },
76
+ "model.layers.0.mlp.up_proj": {
77
+ "in_features": 4096,
78
+ "out_features": 14336,
79
+ "in_factors": [
80
+ 64,
81
+ 64
82
+ ],
83
+ "out_factors": [
84
+ 112,
85
+ 128
86
+ ]
87
+ },
88
+ "model.layers.0.mlp.down_proj": {
89
+ "in_features": 14336,
90
+ "out_features": 4096,
91
+ "in_factors": [
92
+ 112,
93
+ 128
94
+ ],
95
+ "out_factors": [
96
+ 64,
97
+ 64
98
+ ]
99
+ },
100
+ "model.layers.1.self_attn.q_proj": {
101
+ "in_features": 4096,
102
+ "out_features": 4096,
103
+ "in_factors": [
104
+ 64,
105
+ 64
106
+ ],
107
+ "out_factors": [
108
+ 64,
109
+ 64
110
+ ]
111
+ },
112
+ "model.layers.1.self_attn.k_proj": {
113
+ "in_features": 4096,
114
+ "out_features": 1024,
115
+ "in_factors": [
116
+ 64,
117
+ 64
118
+ ],
119
+ "out_factors": [
120
+ 32,
121
+ 32
122
+ ]
123
+ },
124
+ "model.layers.1.self_attn.v_proj": {
125
+ "in_features": 4096,
126
+ "out_features": 1024,
127
+ "in_factors": [
128
+ 64,
129
+ 64
130
+ ],
131
+ "out_factors": [
132
+ 32,
133
+ 32
134
+ ]
135
+ },
136
+ "model.layers.1.self_attn.o_proj": {
137
+ "in_features": 4096,
138
+ "out_features": 4096,
139
+ "in_factors": [
140
+ 64,
141
+ 64
142
+ ],
143
+ "out_factors": [
144
+ 64,
145
+ 64
146
+ ]
147
+ },
148
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