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LightGPT

Text Generation
PyTorch
TensorBoard
ONNX
Safetensors
English
NoPE
Model card Files Metrics Community
Andrew DalPino commited on
Commit
530fce2
·
1 Parent(s): 2947852

Add runs data

Browse files
Files changed (45) hide show
  1. export_model.ipynb +28 -46
  2. model.py +1 -1
  3. requirements.txt +1 -0
  4. runs/.gitignore +0 -2
  5. exports/lightgpt-small.safetensors → runs/pretrain/events.out.tfevents.1736466030.Volluto.81354.0 +2 -2
  6. runs/pretrain/events.out.tfevents.1736466840.Volluto.82624.0 +3 -0
  7. runs/pretrain/events.out.tfevents.1736467719.Volluto.83932.0 +3 -0
  8. runs/pretrain/events.out.tfevents.1736467740.Volluto.84091.0 +3 -0
  9. runs/pretrain/events.out.tfevents.1736487253.Volluto.90438.0 +3 -0
  10. runs/pretrain/events.out.tfevents.1736535683.Volluto.99255.0 +3 -0
  11. runs/pretrain/events.out.tfevents.1736544912.Volluto.104910.0 +3 -0
  12. runs/pretrain/events.out.tfevents.1736554808.Volluto.112600.0 +3 -0
  13. runs/pretrain/events.out.tfevents.1736620787.Volluto.127920.0 +3 -0
  14. runs/pretrain/events.out.tfevents.1736631837.Volluto.141715.0 +3 -0
  15. runs/pretrain/events.out.tfevents.1736631922.Volluto.141955.0 +3 -0
  16. runs/pretrain/events.out.tfevents.1736631984.Volluto.142151.0 +3 -0
  17. runs/pretrain/events.out.tfevents.1736632773.Volluto.143800.0 +3 -0
  18. runs/pretrain/events.out.tfevents.1736652187.Volluto.150882.0 +3 -0
  19. runs/pretrain/events.out.tfevents.1736708815.Volluto.162800.0 +3 -0
  20. runs/pretrain/events.out.tfevents.1736793640.Volluto.174758.0 +3 -0
  21. runs/pretrain/events.out.tfevents.1736794575.Volluto.176720.0 +3 -0
  22. runs/pretrain/events.out.tfevents.1736804694.Volluto.181617.0 +3 -0
  23. runs/pretrain/events.out.tfevents.1736825677.Volluto.196991.0 +3 -0
  24. runs/pretrain/events.out.tfevents.1737054507.Volluto.268046.0 +3 -0
  25. runs/pretrain/events.out.tfevents.1737168101.Volluto.315789.0 +3 -0
  26. runs/pretrain/events.out.tfevents.1737235016.Volluto.326703.0 +3 -0
  27. runs/pretrain/events.out.tfevents.1737257938.Volluto.346932.0 +3 -0
  28. runs/pretrain/events.out.tfevents.1737365338.Volluto.493008.0 +3 -0
  29. runs/pretrain/events.out.tfevents.1737436182.Volluto.604357.0 +3 -0
  30. runs/pretrain/events.out.tfevents.1737517186.Volluto.1230930.0 +3 -0
  31. runs/pretrain/events.out.tfevents.1737530523.Volluto.1322326.0 +3 -0
  32. runs/pretrain/events.out.tfevents.1737574538.Volluto.1655781.0 +3 -0
  33. runs/pretrain/events.out.tfevents.1737609647.Volluto.1934498.0 +3 -0
  34. runs/pretrain/events.out.tfevents.1737656578.Volluto.2361148.0 +3 -0
  35. runs/pretrain/events.out.tfevents.1737790165.Volluto.3650875.0 +3 -0
  36. runs/pretrain/events.out.tfevents.1737856946.Volluto.116091.0 +3 -0
  37. runs/pretrain/events.out.tfevents.1737869637.Volluto.246299.0 +3 -0
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  39. runs/pretrain/events.out.tfevents.1738027332.Volluto.1706741.0 +3 -0
  40. runs/pretrain/events.out.tfevents.1738050871.Volluto.1941035.0 +3 -0
  41. runs/pretrain/events.out.tfevents.1738120997.Volluto.2600473.0 +3 -0
  42. runs/pretrain/events.out.tfevents.1738193815.Volluto.6240.0 +3 -0
  43. runs/pretrain/events.out.tfevents.1738194408.Volluto.13793.0 +3 -0
  44. runs/pretrain/events.out.tfevents.1738221282.Volluto.300674.0 +3 -0
  45. runs/pretrain/events.out.tfevents.1738221313.Volluto.301272.0 +3 -0
export_model.ipynb CHANGED
@@ -9,7 +9,7 @@
9
  },
10
  {
11
  "cell_type": "code",
12
- "execution_count": 10,
13
  "metadata": {},
14
  "outputs": [],
15
  "source": [
@@ -28,7 +28,7 @@
28
  },
29
  {
30
  "cell_type": "code",
31
- "execution_count": 11,
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  "metadata": {},
33
  "outputs": [
34
  {
@@ -64,7 +64,7 @@
64
  },
65
  {
66
  "cell_type": "code",
67
- "execution_count": 12,
68
  "metadata": {},
69
  "outputs": [],
70
  "source": [
@@ -84,38 +84,6 @@
84
  " print(\"LoRA checkpoint loaded successfully\")"
85
  ]
86
  },
87
- {
88
- "cell_type": "markdown",
89
- "metadata": {},
90
- "source": [
91
- "Now, export the model in Safetensors format."
92
- ]
93
- },
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- {
95
- "cell_type": "code",
96
- "execution_count": 13,
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- "metadata": {},
98
- "outputs": [
99
- {
100
- "name": "stdout",
101
- "output_type": "stream",
102
- "text": [
103
- "Model saved to ./exports/lightgpt-small.safetensors\n"
104
- ]
105
- }
106
- ],
107
- "source": [
108
- "from os import path\n",
109
- "\n",
110
- "from safetensors.torch import save_model\n",
111
- "\n",
112
- "safetensors_path = path.join(exports_path, f\"{model_name}.safetensors\")\n",
113
- "\n",
114
- "save_model(model, safetensors_path)\n",
115
- "\n",
116
- "print(f\"Model saved to {safetensors_path}\")"
117
- ]
118
- },
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  {
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  "cell_type": "markdown",
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  "metadata": {},
@@ -125,7 +93,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 14,
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  "metadata": {},
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  "outputs": [
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  {
@@ -152,15 +120,15 @@
152
  "\n",
153
  "example_input = torch.randint(0, model.vocabulary_size - 1, (1, 1024))\n",
154
  "\n",
155
- "model = ONNXModel(model) # Nicer inferencing API\n",
156
  "\n",
157
- "model.eval() # Turn off dropout and other train-time operations\n",
158
  "\n",
159
  "export_options = ExportOptions(\n",
160
  " dynamic_shapes=True\n",
161
  ") # Necessary for variable batch and sequence lengths\n",
162
  "\n",
163
- "onnx_model = dynamo_export(model, example_input, export_options=export_options)\n",
164
  "\n",
165
  "onnx_path = path.join(exports_path, f\"{model_name}.onnx\")\n",
166
  "\n",
@@ -173,12 +141,12 @@
173
  "cell_type": "markdown",
174
  "metadata": {},
175
  "source": [
176
- "Lastly, let's compare the output of PyTorch with the ONNX runtime to see if they are the same."
177
  ]
178
  },
179
  {
180
  "cell_type": "code",
181
- "execution_count": 15,
182
  "metadata": {},
183
  "outputs": [
184
  {
@@ -194,7 +162,7 @@
194
  "\n",
195
  "from numpy.testing import assert_allclose\n",
196
  "\n",
197
- "pytorch_logits = model(example_input).detach().numpy()\n",
198
  "\n",
199
  "session = onnxruntime.InferenceSession(onnx_path, providers=[\"CPUExecutionProvider\"])\n",
200
  "\n",
@@ -213,16 +181,30 @@
213
  "cell_type": "markdown",
214
  "metadata": {},
215
  "source": [
216
- "Lastly, let's adapt the LightGPT PyTorch model to the HuggingFace API."
217
  ]
218
  },
219
  {
220
  "cell_type": "code",
221
- "execution_count": 17,
222
  "metadata": {},
223
- "outputs": [],
 
 
 
 
 
 
 
 
224
  "source": [
225
- "model.model.save_pretrained(path.join(exports_path, model_name))"
 
 
 
 
 
 
226
  ]
227
  }
228
  ],
 
9
  },
10
  {
11
  "cell_type": "code",
12
+ "execution_count": 33,
13
  "metadata": {},
14
  "outputs": [],
15
  "source": [
 
28
  },
29
  {
30
  "cell_type": "code",
31
+ "execution_count": 34,
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  "metadata": {},
33
  "outputs": [
34
  {
 
64
  },
65
  {
66
  "cell_type": "code",
67
+ "execution_count": 35,
68
  "metadata": {},
69
  "outputs": [],
70
  "source": [
 
84
  " print(\"LoRA checkpoint loaded successfully\")"
85
  ]
86
  },
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
87
  {
88
  "cell_type": "markdown",
89
  "metadata": {},
 
93
  },
94
  {
95
  "cell_type": "code",
96
+ "execution_count": 36,
97
  "metadata": {},
98
  "outputs": [
99
  {
 
120
  "\n",
121
  "example_input = torch.randint(0, model.vocabulary_size - 1, (1, 1024))\n",
122
  "\n",
123
+ "onnx_model = ONNXModel(model) # Nicer inferencing API\n",
124
  "\n",
125
+ "onnx_model.eval() # Turn off dropout and other train-time operations\n",
126
  "\n",
127
  "export_options = ExportOptions(\n",
128
  " dynamic_shapes=True\n",
129
  ") # Necessary for variable batch and sequence lengths\n",
130
  "\n",
131
+ "onnx_model = dynamo_export(onnx_model, example_input, export_options=export_options)\n",
132
  "\n",
133
  "onnx_path = path.join(exports_path, f\"{model_name}.onnx\")\n",
134
  "\n",
 
141
  "cell_type": "markdown",
142
  "metadata": {},
143
  "source": [
144
+ "Compare the output of PyTorch with the ONNX runtime to see if they are the same."
145
  ]
146
  },
147
  {
148
  "cell_type": "code",
149
+ "execution_count": 37,
150
  "metadata": {},
151
  "outputs": [
152
  {
 
162
  "\n",
163
  "from numpy.testing import assert_allclose\n",
164
  "\n",
165
+ "pytorch_logits = model.predict(example_input).detach().numpy()\n",
166
  "\n",
167
  "session = onnxruntime.InferenceSession(onnx_path, providers=[\"CPUExecutionProvider\"])\n",
168
  "\n",
 
181
  "cell_type": "markdown",
182
  "metadata": {},
183
  "source": [
184
+ "Lastly, let's export the model in HuggingFace format so that it can be used with the HuggingFace ecosystem."
185
  ]
186
  },
187
  {
188
  "cell_type": "code",
189
+ "execution_count": 39,
190
  "metadata": {},
191
+ "outputs": [
192
+ {
193
+ "name": "stdout",
194
+ "output_type": "stream",
195
+ "text": [
196
+ "Model saved to ./exports/lightgpt-small\n"
197
+ ]
198
+ }
199
+ ],
200
  "source": [
201
+ "from os import path\n",
202
+ "\n",
203
+ "hf_path = path.join(exports_path, model_name)\n",
204
+ "\n",
205
+ "model.save_pretrained(hf_path)\n",
206
+ "\n",
207
+ "print(f\"Model saved to {hf_path}\")"
208
  ]
209
  }
210
  ],
model.py CHANGED
@@ -308,7 +308,7 @@ class LightGPT(Module, PyTorchModelHubMixin):
308
  return completed
309
 
310
 
311
- class LightGPTInstruct(Module):
312
  """
313
  A wrapper for pretrained GPT models that applies a LoRA reparameterization
314
  to the intermediate layers of the network.
 
308
  return completed
309
 
310
 
311
+ class LightGPTInstruct(Module, PyTorchModelHubMixin):
312
  """
313
  A wrapper for pretrained GPT models that applies a LoRA reparameterization
314
  to the intermediate layers of the network.
requirements.txt CHANGED
@@ -9,3 +9,4 @@ tensorboard==2.18.0
9
  safetensors==0.5.2
10
  onnx==1.17.0
11
  onnxruntime==1.20.1
 
 
9
  safetensors==0.5.2
10
  onnx==1.17.0
11
  onnxruntime==1.20.1
12
+ huggingface_hub==0.27.0
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@@ -1,2 +0,0 @@
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- *
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- !.gitignore
 
 
 
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