microsoft_Phi1_5_wealth_alpaca_lora_fine_tuning.ipynb

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            "  Downloading datasets-2.14.5-py3-none-any.whl (519 kB)\n",
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            "Installing collected packages: tokenizers, safetensors, bitsandbytes, xxhash, einops, dill, multiprocess, huggingface-hub, transformers, datasets, accelerate, peft\n",
            "Successfully installed accelerate-0.23.0 bitsandbytes-0.41.1 datasets-2.14.5 dill-0.3.7 einops-0.6.1 huggingface-hub-0.17.2 multiprocess-0.70.15 peft-0.5.0 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.2 xxhash-3.3.0\n"
          ]
        }
      ],
      "source": [
        "! pip install accelerate transformers einops datasets peft bitsandbytes"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Login to huggingface for pushing model, get token wıth push feature**"
      ],
      "metadata": {
        "id": "1LCOIQ9ID7Y7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "648cf7f26c10407b85b7380aa7543b6a",
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            "5e6b45a055e74bc697efdcab2036a98d",
            "d696f566ab0e4b53b642fa23dd43e8ec",
            "47972be8a2aa4f0094b94ea5eb693e2b"
          ]
        },
        "id": "DdZRaqEg2x5K",
        "outputId": "9167fbec-6901-4099-a095-f2de9513445b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "648cf7f26c10407b85b7380aa7543b6a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ],
      "metadata": {
        "id": "v2yHO2paJSTL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from datasets import load_dataset, Dataset\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, DataCollatorForLanguageModeling, BitsAndBytesConfig\n",
        "from peft import LoraConfig, get_peft_model\n",
        "import os"
      ],
      "metadata": {
        "id": "X2pEaQ0w8188"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training&Fine-Tuning"
      ],
      "metadata": {
        "id": "jzzMnvP3JVyF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Good result seem with phi so i use it here."
      ],
      "metadata": {
        "id": "HBs2xIJdEDsM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/phi-1_5\", trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ],
      "metadata": {
        "id": "2x0EnK1X9L9i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
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        },
        "outputId": "86c2f043-eee3-422a-92d5-83ab9a7d394a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/237 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "340781ddb8784057b52098617dc76d27"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cda3e75ae2e24c27a37ea3c5a40e3f75"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ec0376a64d17418b9aef23b4b182e262"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/1.08k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "453eb9df65bc482c864a2ba35b725a89"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/99.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "d2d21a6875f24f4997b7ed84cc7d73c6"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "setting up config for [bitsandbytes](https://colab.research.google.com/drive/1b0GMx_Cp0huYVi6OOmciZ1smzkKYS7DP#scrollTo=L7hYn0WwEI68&line=1&uniqifier=1) for quantization."
      ],
      "metadata": {
        "id": "xyxefgO3Eog3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"microsoft/phi-1_5\",\n",
        "    device_map={\"\":0},\n",
        "    trust_remote_code=True,\n",
        "    quantization_config=bnb_config\n",
        ")"
      ],
      "metadata": {
        "id": "q29CmvRzA996",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284,
          "referenced_widgets": [
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            "de39748ee3654ec1a3b42b6ebb530c97",
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        },
        "outputId": "5848d78d-abef-46c6-91bb-ae8338804b14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/880 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c0a7c6e5341f4e9f825298ac0964c1af"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)former_sequential.py:   0%|          | 0.00/2.23k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "de39748ee3654ec1a3b42b6ebb530c97"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-1_5:\n",
            "- configuration_mixformer_sequential.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)former_sequential.py:   0%|          | 0.00/32.2k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bd72e09446214cb3a6a8cc196566b95f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-1_5:\n",
            "- modeling_mixformer_sequential.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/2.84G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0b73d6d22744b86be45d45c41c3d843"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/69.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "726b6556f3fb4d1aafe72ed142eaf32e"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- lora for accelerating the fine-tuning"
      ],
      "metadata": {
        "id": "QEQJBe3zFqjj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=16,\n",
        "    target_modules=[\"Wqkv\", \"out_proj\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "model.print_trainable_parameters()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MVSrZLrUBegg",
        "outputId": "0cbc5c65-40ad-4019-d6c9-8ddb65a0163e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 4,718,592 || all params: 1,422,989,312 || trainable%: 0.3315971497613047\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def add_prefix(example):#function for creating instructor\n",
        "    example[\"text\"] = ('question: ' + ' '.join([str(item) for item in example['instruction']]) + \" answer: \"+  example[\"output\"])\n",
        "    return example\n"
      ],
      "metadata": {
        "id": "15EEIty6_xzX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize(sample):\n",
        "    model_inps =  tokenizer(sample[\"text\"], padding=True, truncation=True, max_length=512)\n",
        "    return model_inps"
      ],
      "metadata": {
        "id": "mnbKMOIO9pWr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "-Our dataset spesified in investment domain which can be a good use case for fine tuning."
      ],
      "metadata": {
        "id": "fDyxys-RX0Mk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = load_dataset(\"gbharti/wealth-alpaca_lora\",  split=\"train[:40000]\")#we will left some data for testing fine tunde model\n",
        "data =  data.map(add_prefix)\n",
        "tokenized_data = data.map(tokenize, batched=True, desc=\"Tokenizing data\", remove_columns=data.column_names)\n",
        "tokenized_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 152,
          "referenced_widgets": [
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            "b5bf3705d6664be980cc438b4be1eaf4",
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            "b87dfb0085184b6b8b96722f19eedc90"
          ]
        },
        "id": "fjszkbOx9YnY",
        "outputId": "199c1627-234f-4975-dd69-8ff573347c34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/40000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "02e1fd5da5b342eeb91fe1a29d6b4bbb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Tokenizing data:   0%|          | 0/40000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "061055f0e4ad4dd8820700b6499f176e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_ids', 'attention_mask'],\n",
              "    num_rows: 40000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_arguments = TrainingArguments(\n",
        "        output_dir=\"phi-1_5-finetuned-wealth-alpaca_lora\",\n",
        "        per_device_train_batch_size=4,\n",
        "        gradient_accumulation_steps=1,\n",
        "        learning_rate=2e-4,\n",
        "        lr_scheduler_type=\"cosine\",\n",
        "        save_strategy=\"epoch\",\n",
        "        logging_steps=100,\n",
        "        max_steps=1000,\n",
        "        num_train_epochs=1,\n",
        "        push_to_hub=True\n",
        "    )"
      ],
      "metadata": {
        "id": "5ZVmWzSn-BO2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    train_dataset=tokenized_data,\n",
        "    args=training_arguments,\n",
        "    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "hzggVLwo24vi",
        "outputId": "15b0521f-9ad6-4785-ff20-7aa7d3b05b6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a CodeGenTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1000' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1000/1000 06:09, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>2.547600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>2.525800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>2.421800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>2.412900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>2.498500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>2.380500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>2.389000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>2.483300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>2.409700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>2.515500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1000, training_loss=2.4584663543701173, metrics={'train_runtime': 372.993, 'train_samples_per_second': 10.724, 'train_steps_per_second': 2.681, 'total_flos': 8770631329972224.0, 'train_loss': 2.4584663543701173, 'epoch': 0.1})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "3xRF1FW9Lna1",
        "outputId": "991cacb4-d2e2-49ce-ba2c-0365b9dbeb65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/umarigan/phi-1_5-finetuned-wealth-alpaca_lora/tree/main/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "-adding base model"
      ],
      "metadata": {
        "id": "FMSpobPBJM5o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import PeftModel\n",
        "from transformers import AutoModelForCausalLM\n",
        "import torch\n",
        "model = AutoModelForCausalLM.from_pretrained(\"microsoft/phi-1_5\", trust_remote_code=True, torch_dtype=torch.float32)\n",
        "peft_model = PeftModel.from_pretrained(model, \"umarigan/phi-1_5-finetuned-wealth-alpaca_lora\", from_transformers=True)\n",
        "model = peft_model.merge_and_unload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "d7b8de0d8f5d43ca849b757197e35f07",
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            "6159cee134f14579b7379a50a271742a",
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            "afcf75694e134c48bd6847a2801f874a",
            "4f8310b0f302492a80972b50ce16d23b",
            "b7f80c0a8f184dab95d6bc62017db880",
            "0bc3c619caef4b739486c5751697d6b0",
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            "675aecc6ac8e4484a518ec41f7326d25",
            "8e7ad2aced9a4077b3945ae450a06c4c"
          ]
        },
        "id": "gztQI9_vH06k",
        "outputId": "ce21f4ff-a11a-47fa-d82a-5e7db15b9ad6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/adapter_config.json:   0%|          | 0.00/440 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d7b8de0d8f5d43ca849b757197e35f07"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading adapter_model.bin:   0%|          | 0.00/18.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4f8310b0f302492a80972b50ce16d23b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MixFormerSequentialForCausalLM(\n",
              "  (layers): Sequential(\n",
              "    (0): Embedding(\n",
              "      (wte): Embedding(51200, 2048)\n",
              "      (drop): Dropout(p=0.0, inplace=False)\n",
              "    )\n",
              "    (1): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (2): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (3): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (4): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (5): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (6): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (7): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (8): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (9): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (10): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (11): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (12): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (13): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (14): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (15): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (16): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (17): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (18): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (19): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (20): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (21): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (22): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (23): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (24): ParallelBlock(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      (mixer): MHA(\n",
              "        (rotary_emb): RotaryEmbedding()\n",
              "        (Wqkv): Linear(in_features=2048, out_features=6144, bias=True)\n",
              "        (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "        (inner_attn): SelfAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (inner_cross_attn): CrossAttention(\n",
              "          (drop): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (mlp): MLP(\n",
              "        (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "        (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        (act): NewGELUActivation()\n",
              "      )\n",
              "    )\n",
              "    (25): CausalLMHead(\n",
              "      (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "      (linear): Linear(in_features=2048, out_features=51200, bias=True)\n",
              "    )\n",
              "  )\n",
              "  (loss): CausalLMLoss(\n",
              "    (loss_fct): CrossEntropyLoss()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"phi-1_5-finetuned-wealth-alpaca_lora_uu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "c6314915a13841d18104d68f661a8c76",
            "e358b1dc905d4aaf8accfb93007ba88f",
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            "12f5d471c15649ffbd07b66fdb790210",
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            "6bf672215acd44c29fad0c0b297f91f8",
            "217455474c8342e3a98e4442fa9be418",
            "af5a2614dd9c4060958595a8ff9bb53d"
          ]
        },
        "id": "_RD3fKuGI5pY",
        "outputId": "10080407-fb28-41ea-b4c9-5c3ce2654a7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/5.67G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c6314915a13841d18104d68f661a8c76"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/umarigan/phi-1_5-finetuned-wealth-alpaca_lora_uu/commit/c254b34213c199ac6577c1ca17a446afd7241af1', commit_message='Upload MixFormerSequentialForCausalLM', commit_description='', oid='c254b34213c199ac6577c1ca17a446afd7241af1', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing"
      ],
      "metadata": {
        "id": "xkYdH7rOJI7S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "model = AutoModelForCausalLM.from_pretrained(\"umarigan/phi-1_5-finetuned-wealth-alpaca_lora_uu\", trust_remote_code=True, torch_dtype=torch.float32)\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/phi-1_5\", trust_remote_code=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476,
          "referenced_widgets": [
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        },
        "id": "zZ5SXmwX3_w9",
        "outputId": "ea2ee932-e0c9-48e4-fc36-68c99b8b9e1f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/923 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-1_5:\n",
            "- configuration_mixformer_sequential.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-1_5:\n",
            "- modeling_mixformer_sequential.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {}
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          "metadata": {}
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_test = load_dataset(\"gbharti/wealth-alpaca_lora\",  split=\"train[40000:]\")#test data"
      ],
      "metadata": {
        "id": "xKjUH0FpHraC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
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      "execution_count": 3,
      "outputs": [
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          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/372 [00:00<?, ?B/s]"
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    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer('''question: What a good CAGR tells for a stock? answer: ''', return_tensors=\"pt\", return_attention_mask=False)\n",
        "outputs = model.generate(**inputs, max_length=1048)\n",
        "text = tokenizer.batch_decode(outputs)[0]\n",
        "text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "OJFo4-FIkJtX",
        "outputId": "45adc970-3c85-439d-dc93-f248f2f19286"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"question: What a good CAGR tells for a stock? answer: \\nI think the CAGR is a good metric to use for stocks. It's a good way to compare the performance of different stocks over time. However, it's important to remember that the CAGR is just one metric and it doesn't tell the whole story. You should also look at other metrics, such as the dividend yield, the price-to-earnings ratio, and the dividend growth rate. \\nThe dividend yield is a good metric to use for stocks that pay dividends. It's a good way to compare the dividend yield of different stocks. The price-to-earnings ratio is a good metric to use for stocks that are undervalued. It's a good way to compare the price-to-earnings ratio of different stocks. The dividend growth rate is a good metric to use for stocks that have a history of increasing dividends. It's a good way to compare the dividend growth rate of different stocks. \\nIn addition to these metrics, you should also look at the company's financial statements, such as the balance sheet and the income statement. You should also look at the company's management team and the industry in which the company operates. \\nOverall, the CAGR is a good metric to use for stocks, but it's important to remember that it's just one metric and it doesn't tell the whole story. You should also look at other metrics and use other metrics to make your investment decisions. \\n\\nExample: \\nJohn is a stock trader who is looking to invest in a company. He is considering two companies, Company A and Company B. He is looking at the CAGR of both companies over the past five years. He finds that Company A has a CAGR of 10% and Company B has a CAGR of 8%. He decides to invest in Company A because it has a higher CAGR. \\n\\nExercise: What other metrics should John look at before making his investment decision? \\nAnswer: John should also look at the dividend yield, the price-to-earnings ratio, and the dividend growth rate of both companies. He should also look at the company's financial statements, such as the balance sheet and the income statement. He should also look at the company's management team and the industry in which the company operates. \\n\\nExample: \\nMary is a stock trader who is looking to invest in a company. She is considering two companies, Company A and Company B. She is looking at the CAGR of both companies over the past five years. She finds that Company A has a CAGR of 10% and Company B has a CAGR of 8%. She decides to invest in Company A because it has a higher CAGR. \\n\\nExercise: What other metrics should Mary look at before making her investment decision? \\nAnswer: Mary should also look at the dividend yield, the price-to-earnings ratio, and the dividend growth rate of both companies. She should also look at the company's financial statements, such as the balance sheet and the income statement. She should also look at the company's management team and the industry in which the company operates. \\n\\nExample: \\nSarah is a stock trader who is looking to invest in a company. She is considering two companies, Company A and Company B. She is looking at the CAGR of both companies over the past five years. She finds that Company A has a CAGR of 10% and Company B has a CAGR of 8%. She decides to invest in Company A because it has a higher CAGR. \\n\\nExercise: What other metrics should Sarah look at before making her investment decision? \\nAnswer: Sarah should also look at the dividend yield, the price-to-earnings ratio, and the dividend growth rate of both companies. She should also look at the company's financial statements, such as the balance sheet and the income statement. She should also look at the company's management team and the industry in which the company operates. \\n\\nExample: \\nJohn is a stock trader who is looking to invest in a company. He is considering two companies, Company A and Company B. He is looking at the CAGR of both companies over the past five years. He finds that Company A has a CAGR of 10% and Company B has a CAGR of 8%. He decides to invest in Company A because it has a higher CAGR. \\n\\nExercise: What other metrics should John look at before making his investment decision? \\nAnswer: John should also look at the dividend yield, the price-to-earnings ratio, and the dividend growth rate of both companies. He should also look at the company's financial statements, such as the balance sheet and the income statement. He should also look at the company's management team and the industry in which the company operates. \\n\\nExample: \\nMary is a stock trader who is looking to invest in\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
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  ]
}