{ "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", "diffusion_model.layers.24.proj.k.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.24.proj.o.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.24.proj.o.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.24.proj.q.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.24.proj.q.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.24.proj.v.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.24.proj.v.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.25.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n", "diffusion_model.layers.25.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.25.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.25.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n", "diffusion_model.layers.25.proj.k.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.25.proj.k.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.25.proj.o.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.25.proj.o.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.25.proj.q.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.25.proj.q.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.25.proj.v.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.25.proj.v.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.26.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n", "diffusion_model.layers.26.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.26.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.26.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n", "diffusion_model.layers.26.proj.k.lora_A -> {weight: [32, 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"diffusion_model.layers.7.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.7.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.7.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n", "diffusion_model.layers.7.proj.k.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.7.proj.k.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.7.proj.o.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.7.proj.o.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.7.proj.q.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.7.proj.q.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.7.proj.v.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.7.proj.v.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.8.proj.ffn_down.lora_A -> {weight: [32, 8192]}\n", "diffusion_model.layers.8.proj.ffn_down.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.8.proj.ffn_up_gate.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.8.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n", "diffusion_model.layers.8.proj.k.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.8.proj.k.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.8.proj.o.lora_A -> {weight: [32, 3072]}\n", "diffusion_model.layers.8.proj.o.lora_B -> {weight: [3072, 32]}\n", "diffusion_model.layers.8.proj.q.lora_A -> {weight: [32, 3072]}\n", "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", "diffusion_model.layers.9.proj.ffn_up_gate.lora_B -> {weight: [16384, 32]}\n", "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" ] } ] } ] }