Upload llllmmm.ipynb

llllmmm.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "id": "initial_id",
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-07-01T11:08:00.253851Z",
+          "start_time": "2024-07-01T11:08:00.067738Z"
+        },
+        "id": "initial_id"
+      },
+      "source": [
+        "import torch\n",
+        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
+      ],
+      "outputs": [],
+      "execution_count": 2
+    },
+    {
+      "cell_type": "code",
+      "id": "df5ce2489db64f8d",
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-07-01T11:08:15.731889Z",
+          "start_time": "2024-07-01T11:08:03.640950Z"
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 205,
+          "referenced_widgets": [
+            "6ecdc71d497b4ab7bc6dca2ace0bd656",
+            "2af56a7c045d4bc294c6cb6d362a8120",
+            "f5611ea1eab5406eb5796ffed1218a0c",
+            "68855bdfbeed46e7b7e3d82a9b4c0988",
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+            "f98f7a5726d6434d9087fe521e136795",
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+            "d4a74b31d8064a5bba43849ed4b6658b",
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+            "0ff317589550455f8eef5021912dd27c",
+            "7e110f5c777442c4aab4ea06a27a3dc1",
+            "ae1843d43b0f4199ba61e8c9e962f2b3",
+            "d72cd66e520a4eea83c06d7df23ccc0e"
+          ]
+        },
+        "id": "df5ce2489db64f8d",
+        "outputId": "34a739e5-75cd-4f03-b6b2-15e4b767bb64"
+      },
+      "source": [
+        "tokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-xl\")\n",
+        "model = AutoModelForSeq2SeqLM.from_pretrained(\"google/flan-t5-xl\")"
+      ],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6ecdc71d497b4ab7bc6dca2ace0bd656"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "46756e51804c48ebbabee753ea455457"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 3
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.to(device)"
+      ],
+      "metadata": {
+        "collapsed": true,
+        "id": "WJ623uJ0XR60",
+        "outputId": "54381bb6-1b49-4478-e5b8-b20b03dc388a",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "id": "WJ623uJ0XR60",
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "T5ForConditionalGeneration(\n",
+              "  (shared): Embedding(32128, 2048)\n",
+              "  (encoder): T5Stack(\n",
+              "    (embed_tokens): Embedding(32128, 2048)\n",
+              "    (block): ModuleList(\n",
+              "      (0): T5Block(\n",
+              "        (layer): ModuleList(\n",
+              "          (0): T5LayerSelfAttention(\n",
+              "            (SelfAttention): T5Attention(\n",
+              "              (q): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (k): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (v): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (o): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (relative_attention_bias): Embedding(32, 32)\n",
+              "            )\n",
+              "            (layer_norm): T5LayerNorm()\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "          )\n",
+              "          (1): T5LayerFF(\n",
+              "            (DenseReluDense): T5DenseGatedActDense(\n",
+              "              (wi_0): Linear(in_features=2048, out_features=5120, bias=False)\n",
+              "              (wi_1): Linear(in_features=2048, out_features=5120, bias=False)\n",
+              "              (wo): Linear(in_features=5120, out_features=2048, bias=False)\n",
+              "              (dropout): Dropout(p=0.1, inplace=False)\n",
+              "              (act): NewGELUActivation()\n",
+              "            )\n",
+              "            (layer_norm): T5LayerNorm()\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "      (1-23): 23 x T5Block(\n",
+              "        (layer): ModuleList(\n",
+              "          (0): T5LayerSelfAttention(\n",
+              "            (SelfAttention): T5Attention(\n",
+              "              (q): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (k): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (v): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (o): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "            )\n",
+              "            (layer_norm): T5LayerNorm()\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "          )\n",
+              "          (1): T5LayerFF(\n",
+              "            (DenseReluDense): T5DenseGatedActDense(\n",
+              "              (wi_0): Linear(in_features=2048, out_features=5120, bias=False)\n",
+              "              (wi_1): Linear(in_features=2048, out_features=5120, bias=False)\n",
+              "              (wo): Linear(in_features=5120, out_features=2048, bias=False)\n",
+              "              (dropout): Dropout(p=0.1, inplace=False)\n",
+              "              (act): NewGELUActivation()\n",
+              "            )\n",
+              "            (layer_norm): T5LayerNorm()\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "    )\n",
+              "    (final_layer_norm): T5LayerNorm()\n",
+              "    (dropout): Dropout(p=0.1, inplace=False)\n",
+              "  )\n",
+              "  (decoder): T5Stack(\n",
+              "    (embed_tokens): Embedding(32128, 2048)\n",
+              "    (block): ModuleList(\n",
+              "      (0): T5Block(\n",
+              "        (layer): ModuleList(\n",
+              "          (0): T5LayerSelfAttention(\n",
+              "            (SelfAttention): T5Attention(\n",
+              "              (q): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (k): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (v): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (o): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (relative_attention_bias): Embedding(32, 32)\n",
+              "            )\n",
+              "            (layer_norm): T5LayerNorm()\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "          )\n",
+              "          (1): T5LayerCrossAttention(\n",
+              "            (EncDecAttention): T5Attention(\n",
+              "              (q): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (k): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (v): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (o): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "            )\n",
+              "            (layer_norm): T5LayerNorm()\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "          )\n",
+              "          (2): T5LayerFF(\n",
+              "            (DenseReluDense): T5DenseGatedActDense(\n",
+              "              (wi_0): Linear(in_features=2048, out_features=5120, bias=False)\n",
+              "              (wi_1): Linear(in_features=2048, out_features=5120, bias=False)\n",
+              "              (wo): Linear(in_features=5120, out_features=2048, bias=False)\n",
+              "              (dropout): Dropout(p=0.1, inplace=False)\n",
+              "              (act): NewGELUActivation()\n",
+              "            )\n",
+              "            (layer_norm): T5LayerNorm()\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "      (1-23): 23 x T5Block(\n",
+              "        (layer): ModuleList(\n",
+              "          (0): T5LayerSelfAttention(\n",
+              "            (SelfAttention): T5Attention(\n",
+              "              (q): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (k): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (v): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (o): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "            )\n",
+              "            (layer_norm): T5LayerNorm()\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "          )\n",
+              "          (1): T5LayerCrossAttention(\n",
+              "            (EncDecAttention): T5Attention(\n",
+              "              (q): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (k): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (v): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "              (o): Linear(in_features=2048, out_features=2048, bias=False)\n",
+              "            )\n",
+              "            (layer_norm): T5LayerNorm()\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "          )\n",
+              "          (2): T5LayerFF(\n",
+              "            (DenseReluDense): T5DenseGatedActDense(\n",
+              "              (wi_0): Linear(in_features=2048, out_features=5120, bias=False)\n",
+              "              (wi_1): Linear(in_features=2048, out_features=5120, bias=False)\n",
+              "              (wo): Linear(in_features=5120, out_features=2048, bias=False)\n",
+              "              (dropout): Dropout(p=0.1, inplace=False)\n",
+              "              (act): NewGELUActivation()\n",
+              "            )\n",
+              "            (layer_norm): T5LayerNorm()\n",
+              "            (dropout): Dropout(p=0.1, inplace=False)\n",
+              "          )\n",
+              "        )\n",
+              "      )\n",
+              "    )\n",
+              "    (final_layer_norm): T5LayerNorm()\n",
+              "    (dropout): Dropout(p=0.1, inplace=False)\n",
+              "  )\n",
+              "  (lm_head): Linear(in_features=2048, out_features=32128, bias=False)\n",
+              ")"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from transformers import Trainer, TrainingArguments\n",
+        "\n",
+        "# Define training arguments\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir=\"./output\",\n",
+        "    num_train_epochs=3,\n",
+        "    per_device_train_batch_size=8,\n",
+        "    # ... other training arguments\n",
+        ")\n",
+        "\n",
+        "# Create a Trainer object\n",
+        "trainer = Trainer(\n",
+        "    model=model,\n",
+        "    args=training_args,\n",
+        "    train_dataset=Financial.csv, # Load your training dataset\n",
+        ")\n",
+        "\n",
+        "# Start training\n",
+        "trainer.train()"
+      ],
+      "metadata": {
+        "id": "j_Y33_fMfGd9"
+      },
+      "id": "j_Y33_fMfGd9",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "id": "7ce8ee88e61ac738",
+      "metadata": {
+        "ExecuteTime": {
+          "end_time": "2024-07-01T11:08:50.346303Z",
+          "start_time": "2024-07-01T11:08:50.336252Z"
+        },
+        "id": "7ce8ee88e61ac738"
+      },
+      "source": [
+        "def get_response(prompt, max_new_tokens=50):\n",
+        "    inputs = tokenizer(prompt, return_tensors=\"pt\").to(device)\n",
+        "    outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, temperature= 0.0001, do_sample=True)\n",
+        "    response = tokenizer.decode(outputs[0], skip_special_tokens=True) # Use indexing instead of calling\n",
+        "    return response"
+      ],
+      "outputs": [],
+      "execution_count": 15
+    },
+    {
+      "metadata": {
+        "jupyter": {
+          "is_executing": true
+        },
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "de9f0fcc6dc9fa82",
+        "outputId": "91cc9e78-4e4e-4f29-99b4-94fdaedccd1b"
+      },
+      "cell_type": "code",
+      "source": [
+        "prompt ='What is capital of Madhya Pradesh'\n",
+        "get_response(prompt, max_new_tokens=50)"
+      ],
+      "id": "de9f0fcc6dc9fa82",
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'bhopal'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ],
+      "execution_count": 34
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "s77Nu-3UaeZ9"
+      },
+      "id": "s77Nu-3UaeZ9",
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.4"
+    },
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+      "gpuType": "T4"
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