Upload create_hidream_sdnq.ipynb

colab_notebooks/create_hidream_sdnq.ipynb

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+            "89689e97531d4f4db2937d31d0fb6538",
+            "846cd637017d46ee96a519e2f47a833b",
+            "63bb7f100c464dd8bd52340fc94f51f1",
+            "1164b72288d2409aaed65050b9a9470d",
+            "1c643ffb974849f3933cc1c52ba014e9",
+            "e697d39f24be4ca18f4a438b899f21eb"
           ]
         },
         "id": "d97388b1",
-        "outputId": "e7398874-c06f-48cb-9a5b-bcf575c52ead"
+        "outputId": "3bd82301-029f-4ab0-ab24-23f0040c29c6"
       },
       "source": [
         "from huggingface_hub import snapshot_download\n",
@@ -6457,7 +6810,7 @@
         "print(f\"SDNQ Model device: {next(sdnq_model.parameters()).device}\")\n",
         "print(f\"SDNQ Model dtype:  {next(sdnq_model.parameters()).dtype}\")"
       ],
-      "execution_count": null,
+      "execution_count": 8,
       "outputs": [
         {
           "output_type": "stream",
@@ -6470,12 +6823,26 @@
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              "Fetching 7 files:   0%|          | 0/7 [00:00<?, ?it/s]"
+              "Fetching 17 files:   0%|          | 0/17 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b2d96c62ece74d10ae8cfa0368bdef5d"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "chat_template.json: 0.00B [00:00, ?B/s]"
             ],
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "db161b32e9274be58b559b187b6d292a"
+              "model_id": "2bc646b6c1224558b1513c2e8d3aeec4"
             }
           },
           "metadata": {}
@@ -6484,12 +6851,12 @@
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              "model-00002-of-00003.safetensors:   0%|          | 0.00/4.34G [00:00<?, ?B/s]"
+              "merges.txt: 0.00B [00:00, ?B/s]"
             ],
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "46453419213a433cb3ed6fe07ba5843d"
+              "model_id": "8c53093a52d249a4b10e8dad950aef62"
             }
           },
           "metadata": {}
@@ -6498,12 +6865,12 @@
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              "model-00003-of-00003.safetensors:   0%|          | 0.00/2.49G [00:00<?, ?B/s]"
+              "generation_config.json:   0%|          | 0.00/269 [00:00<?, ?B/s]"
             ],
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "12d20ba2e2024f69af3b65799894601b"
+              "model_id": "d2ea7cdbe39f421eac88fd60c07832aa"
             }
           },
           "metadata": {}
@@ -6512,12 +6879,12 @@
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              "model-00001-of-00003.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
+              "configuration.json:   0%|          | 0.00/73.0 [00:00<?, ?B/s]"
             ],
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "c0f9d65172b848e291ea17a1da321f3c"
+              "model_id": "0517d3ca8f754dda9c05c58df468f389"
             }
           },
           "metadata": {}
@@ -6526,12 +6893,12 @@
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              "generation_config.json:   0%|          | 0.00/213 [00:00<?, ?B/s]"
+              "tokenizer.json: 0.00B [00:00, ?B/s]"
             ],
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "4b6d4113ffba46d5a7690d762ea224ef"
+              "model_id": "b4dbd23ca854403b98b3550f10672c3d"
             }
           },
           "metadata": {}
@@ -6540,12 +6907,12 @@
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              "config.json: 0.00B [00:00, ?B/s]"
+              "tokenizer_config.json: 0.00B [00:00, ?B/s]"
             ],
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "0c272deca15c438c95eb50401e2239c2"
+              "model_id": "067b7146c43d409d8279ba5f1add3d37"
             }
           },
           "metadata": {}
@@ -6554,29 +6921,71 @@
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              ".gitattributes:   0%|          | 0.00/278 [00:00<?, ?B/s]"
+              "vocab.json: 0.00B [00:00, ?B/s]"
             ],
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "5abd62c5fdfd41a89c1d7e4533dd7c17"
+              "model_id": "96eab2db9f1c47e3be7f05e17dd4bc2e"
             }
           },
           "metadata": {}
         },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "✅ SDNQ model downloaded.\n",
+            "Loading processor for SDNQ model...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "`torch_dtype` is deprecated! Use `dtype` instead!\n",
+            "Unknown quantization type, got sdnq - supported types are: ['awq', 'bitsandbytes_4bit', 'bitsandbytes_8bit', 'gptq', 'aqlm', 'quanto', 'quark', 'fp_quant', 'eetq', 'higgs', 'hqq', 'compressed-tensors', 'fbgemm_fp8', 'torchao', 'bitnet', 'vptq', 'spqr', 'fp8', 'auto-round', 'mxfp4']. Hence, we will skip the quantization. To remove the warning, you can delete the quantization_config attribute in config.json\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Loading SDNQ model...\n"
+          ]
+        },
         {
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              "model.safetensors.index.json: 0.00B [00:00, ?B/s]"
+              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
             ],
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "8f11f5be18a04a41a4fdd5f290e430e0"
+              "model_id": "87e5466584114b6daf247e76b485e20b"
             }
           },
           "metadata": {}
+        },
+        {
+          "output_type": "error",
+          "ename": "RuntimeError",
+          "evalue": "Error(s) in loading state_dict for Linear:\n\tsize mismatch for weight: copying a param with shape torch.Size([6291456, 5]) from checkpoint, the shape in current model is torch.Size([4096, 12288]).",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipykernel_3788/3308593350.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Loading SDNQ model...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m sdnq_model = Qwen3VLForConditionalGeneration.from_pretrained(\n\u001b[0m\u001b[1;32m     24\u001b[0m     \u001b[0msdnq_local_download_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0mtorch_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# Ensure correct dtype if needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36m_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m         \u001b[0mold_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 277\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    278\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_default_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mold_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   5046\u001b[0m                 \u001b[0moffload_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5047\u001b[0m                 \u001b[0merror_msgs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5048\u001b[0;31m             \u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load_pretrained_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5049\u001b[0m                 \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5050\u001b[0m                 \u001b[0mstate_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36m_load_pretrained_model\u001b[0;34m(cls, model, state_dict, checkpoint_files, pretrained_model_name_or_path, ignore_mismatched_sizes, sharded_metadata, device_map, disk_offload_folder, dtype, hf_quantizer, keep_in_fp32_regex, device_mesh, key_mapping, weights_only)\u001b[0m\n\u001b[1;32m   5466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5467\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5468\u001b[0;31m                 \u001b[0m_error_msgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisk_offload_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_shard_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5469\u001b[0m                 \u001b[0merror_msgs\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0m_error_msgs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5470\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mload_shard_file\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m    841\u001b[0m     \u001b[0;31m# Skip it with fsdp on ranks other than 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    842\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mis_fsdp_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_local_dist_rank_0\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_quantized\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 843\u001b[0;31m         disk_offload_index = _load_state_dict_into_meta_model(\n\u001b[0m\u001b[1;32m    844\u001b[0m             \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    845\u001b[0m             \u001b[0mstate_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;31m# pyrefly: ignore [bad-context-manager]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36m_load_state_dict_into_meta_model\u001b[0;34m(model, state_dict, shard_file, reverse_renaming_mapping, device_map, disk_offload_folder, disk_offload_index, hf_quantizer, keep_in_fp32_regex, device_mesh)\u001b[0m\n\u001b[1;32m    768\u001b[0m                     \u001b[0mparam_device\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"cpu\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_local_dist_rank_0\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"meta\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 770\u001b[0;31m                 \u001b[0m_load_parameter_into_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    771\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    772\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36m_load_parameter_into_model\u001b[0;34m(model, param_name, tensor)\u001b[0m\n\u001b[1;32m    665\u001b[0m     \u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_module_from_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m     \u001b[0;31m# This will check potential shape mismatch if skipped before\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 667\u001b[0;31m     \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mparam_type\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0massign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mload_state_dict\u001b[0;34m(self, state_dict, strict, assign)\u001b[0m\n\u001b[1;32m   2633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2634\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msgs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2635\u001b[0;31m             raise RuntimeError(\n\u001b[0m\u001b[1;32m   2636\u001b[0m                 \"Error(s) in loading state_dict for {}:\\n\\t{}\".format(\n\u001b[1;32m   2637\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\\n\\t\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msgs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for Linear:\n\tsize mismatch for weight: copying a param with shape torch.Size([6291456, 5]) from checkpoint, the shape in current model is torch.Size([4096, 12288])."
+          ]
         }
       ]
     },