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"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "64310d2f",
"outputId": "6e5e3be2-8d70-44f2-ac23-1078f9f36390"
},
"source": [
"pip install safetensors"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: safetensors in /usr/local/lib/python3.13/dist-packages (0.8.0)\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a1fcf785",
"outputId": "943b05c5-978f-4a42-eeea-1b96a07e9146"
},
"source": [
"from safetensors import safe_open\n",
"\n",
"file_path = '/content/drive/MyDrive/philosophical-tetsu-e1-moss-image-sft-epoch-01.safetensors'\n",
"\n",
"with safe_open(file_path, framework='pt', device='cpu') as f:\n",
" keys = sorted(f.keys())\n",
"\n",
" # Group by the component (e.g. diffusion_model.layers.0.proj.ffn_down)\n",
" grouped = {}\n",
" for key in keys:\n",
" tensor = f.get_tensor(key)\n",
" shape = list(tensor.shape)\n",
"\n",
" # Separate name components\n",
" parts = key.split('.')\n",
" # Find where 'lora_A' or 'lora_B' begins to group parent components\n",
" sub_part_idx = next(i for i, part in enumerate(parts) if 'lora_' in part or part == 'weight')\n",
" parent_group = '.'.join(parts[:sub_part_idx])\n",
" sub_group = '.'.join(parts[sub_part_idx:])\n",
"\n",
" if parent_group not in grouped:\n",
" grouped[parent_group] = []\n",
" grouped[parent_group].append(f\"{sub_group}: {shape}\")\n",
"\n",
"# Print each major parent group with its subcategories collapsed onto a single line\n",
"for parent, dimensions in sorted(grouped.items()):\n",
" print(f\"{parent} -> {{ {', '.join(dimensions)} }}\")"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"diffusion_model.layers.0.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.0.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.0.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.0.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.0.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.0.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.0.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.0.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.0.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.0.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.0.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.0.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.1.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.1.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.1.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.1.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.1.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.1.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.1.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.1.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.1.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.1.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.1.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.1.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.10.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.10.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.10.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.10.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.10.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.10.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.10.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.10.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.10.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.10.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.10.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.10.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.11.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.11.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.11.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.11.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.11.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.11.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.11.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.11.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.11.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.11.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.11.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.11.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.12.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.12.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.12.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.12.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.12.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.12.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.12.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.12.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.12.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.12.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.12.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.12.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.13.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.13.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.13.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.13.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.13.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.13.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.13.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.13.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.13.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.13.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.13.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.13.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.14.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.14.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.14.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.14.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.14.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.14.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.14.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.14.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.14.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.14.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.14.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.14.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.15.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.15.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.15.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.15.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.15.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.15.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.15.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.15.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.15.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.15.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.15.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.15.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.16.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.16.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.16.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.16.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.16.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.16.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.16.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.16.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.16.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.16.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.16.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.16.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.17.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.17.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.17.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.17.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.17.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.17.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.17.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.17.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.17.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.17.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.17.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.17.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.18.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.18.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.18.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.18.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.18.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.18.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.18.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.18.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.18.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.18.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.18.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.18.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.19.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.19.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.19.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.19.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.19.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.19.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.19.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.19.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.19.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.19.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.19.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.19.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.2.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.2.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.2.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.2.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.2.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.2.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.2.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.2.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.2.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.2.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.2.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.2.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.20.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.20.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.20.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.20.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.20.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.20.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.20.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.20.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.20.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.20.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.20.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.20.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.21.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.21.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.21.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.21.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.21.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.21.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.21.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.21.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.21.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.21.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.21.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.21.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.23.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.23.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.23.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.23.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.23.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.23.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.23.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.23.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.23.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.23.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.23.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.23.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.24.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.24.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.24.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.24.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n",
"diffusion_model.layers.24.proj.k.lora_A -> {weight: [32, 3072]}\n",
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"diffusion_model.layers.6.proj.k.lora_A -> {weight: [32, 3072]}\n",
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"diffusion_model.layers.6.proj.o.lora_A -> {weight: [32, 3072]}\n",
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"diffusion_model.layers.6.proj.q.lora_A -> {weight: [32, 3072]}\n",
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"diffusion_model.layers.7.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
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"diffusion_model.layers.8.proj.k.lora_B -> {weight: [3072, 32]}\n",
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"diffusion_model.layers.8.proj.o.lora_B -> {weight: [3072, 32]}\n",
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"diffusion_model.layers.8.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.8.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.8.proj.v.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.9.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n",
"diffusion_model.layers.9.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.9.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n",
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"diffusion_model.layers.9.proj.k.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.9.proj.k.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.9.proj.o.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.9.proj.o.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.9.proj.q.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.9.proj.q.lora_B -> {weight: [3072, 32]}\n",
"diffusion_model.layers.9.proj.v.lora_A -> {weight: [32, 3072]}\n",
"diffusion_model.layers.9.proj.v.lora_B -> {weight: [3072, 32]}\n"
]
}
]
}
]
} |