Upload llllmmm.ipynb

llllmmm.ipynb

@@ -13,11 +13,10 @@
       "source": [
         "import torch\n",
         "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments\n",
-        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-        "modelName=\"google/flan-t5-xl\""
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
       ],
       "outputs": [],
-      "execution_count": 11
+      "execution_count": 2
     },
     {
       "cell_type": "code",
@@ -29,71 +28,38 @@
         },
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 336,
+          "height": 205,
           "referenced_widgets": [
-            "862cf57104f9417c89304dd4dabe423b",
-            "dd0486901a544b3b9a2241a7c2687e45",
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+            "f98f7a5726d6434d9087fe521e136795",
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+            "d72cd66e520a4eea83c06d7df23ccc0e"
           ]
         },
         "id": "df5ce2489db64f8d",
-        "outputId": "837fe222-f0e5-4c9a-bc94-bb3d83414c29"
+        "outputId": "34a739e5-75cd-4f03-b6b2-15e4b767bb64"
       },
       "source": [
-        "tokenizer = AutoTokenizer.from_pretrained(modelName)\n",
-        "model = AutoModelForSeq2SeqLM.from_pretrained(modelName).to(device)"
+        "tokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-xl\")\n",
+        "model = AutoModelForSeq2SeqLM.from_pretrained(\"google/flan-t5-xl\")"
       ],
       "outputs": [
         {
@@ -105,8 +71,6 @@
             "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",
-            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
             "  warnings.warn(\n"
           ]
         },
@@ -114,40 +78,12 @@
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              "config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "862cf57104f9417c89304dd4dabe423b"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "ea1b0a22fbc1463aba924049c9e14f01"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
+              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
             ],
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "aee8a770b18341de904a7e2bbb019424"
+              "model_id": "6ecdc71d497b4ab7bc6dca2ace0bd656"
             }
           },
           "metadata": {}
@@ -156,32 +92,215 @@
           "output_type": "display_data",
           "data": {
             "text/plain": [
-              "model.safetensors:   0%|          | 0.00/2.95G [00:00<?, ?B/s]"
+              "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": "b4835d10c5cf45b7b5d00cc1f4c7224b"
+              "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": "display_data",
+          "output_type": "execute_result",
           "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": "698856b6905d4f82b59e95851d9ae534"
-            }
+              "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": {}
+          "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()"
       ],
-      "execution_count": 2
+      "metadata": {
+        "id": "j_Y33_fMfGd9"
+      },
+      "id": "j_Y33_fMfGd9",
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -194,14 +313,14 @@
         "id": "7ce8ee88e61ac738"
       },
       "source": [
-        "def get_llama2_response(prompt, max_new_tokens=100):\n",
-        "    inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
-        "    outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, temperature= 1)\n",
-        "    response = tokenizer.decode(outputs[0], skip_special_tokens=True ,clean_up_tokenization_spaces=True) # Use indexing instead of calling\n",
+        "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": 7
+      "execution_count": 15
     },
     {
       "metadata": {
@@ -210,15 +329,15 @@
         },
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 70
+          "height": 36
         },
         "id": "de9f0fcc6dc9fa82",
-        "outputId": "c860d3e9-d630-491c-a837-dc84ff8be5ac"
+        "outputId": "91cc9e78-4e4e-4f29-99b4-94fdaedccd1b"
       },
       "cell_type": "code",
       "source": [
-        "prompt =\"Explain How TO Ride A Horse:\"\n",
-        "get_llama2_response(prompt, max_new_tokens=100)"
+        "prompt ='What is capital of Madhya Pradesh'\n",
+        "get_response(prompt, max_new_tokens=50)"
       ],
       "id": "de9f0fcc6dc9fa82",
       "outputs": [
@@ -226,23 +345,32 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "'- How TO RIDE A Horse: How TO How TO Ride A Horse: How TO Ride A Horse: How TO Ride A Horse: How TO Ride A Horse: How TO Ride A Horse: How TO Ride A Horse: How TO Ride A Horse: How TO Ride A Horse: How TO Ride A Horse:. Explain How TO Ride Explain How TO Ride A Horse:::::::: Explain How'"
+              "'bhopal'"
             ],
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "string"
             }
           },
           "metadata": {},
-          "execution_count": 10
+          "execution_count": 34
         }
       ],
-      "execution_count": 10
+      "execution_count": 34
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "s77Nu-3UaeZ9"
+      },
+      "id": "s77Nu-3UaeZ9",
+      "execution_count": null,
+      "outputs": []
     }
   ],
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