Upload Finetuning.ipynb

Finetuning.ipynb

@@ -0,0 +1,2179 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mNnkgBq7Q3EU",
+        "outputId": "a5572244-c21b-4d30-9bb9-6a7333b22b65"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.9/133.9 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.9/7.9 MB\u001b[0m \u001b[31m64.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m103.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m261.4/261.4 kB\u001b[0m \u001b[31m34.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m55.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m100.0/100.0 kB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m311.7/311.7 kB\u001b[0m \u001b[31m36.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m106.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m88.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.2/307.2 kB\u001b[0m \u001b[31m34.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Building wheel for flash-attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m93.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.6/190.6 kB\u001b[0m \u001b[31m25.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m248.6/248.6 kB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q -U trl transformers flash-attn tensor_parallel accelerate git+https://github.com/huggingface/peft.git\n",
+        "!pip install -q datasets bitsandbytes einops wandb"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Rnqmq7amRrU8"
+      },
+      "source": [
+        "## Dataset\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0X3kHnskSWU4"
+      },
+      "outputs": [],
+      "source": [
+        "import random\n",
+        "\n",
+        "from datasets import load_dataset\n",
+        "\n",
+        "dataset_name = 'codys12/Pathway'\n",
+        "dataset = load_dataset(dataset_name, split='train')\n",
+        "\n",
+        "full_dataset = dataset#['text']\n",
+        "\n",
+        "# random.seed(43)\n",
+        "# random_indexes = random.sample(range(len(full_dataset)), 200)\n",
+        "\n",
+        "# test_dataset = dataset.select(random_indexes)\n",
+        "# train_dataset = dataset.select([i for i in range(len(dataset)) if i not in random_indexes])\n",
+        "\n",
+        "# print(f\"Full dataset size: {len(dataset)}\")\n",
+        "# print(f\"Test dataset size: {len(test_dataset)}\")\n",
+        "# print(f\"Train dataset size: {len(train_dataset)}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rjOMoSbGSxx9"
+      },
+      "source": [
+        "## Loading the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "c7312aaf3b1042e49b2ce9ffcd458fe4",
+            "16305f29346e4458be44270922757d82",
+            "7ccfa3162d84410eabe33dc00d02a941",
+            "82d54fe358884c558c865b7529ff3415",
+            "ae7c8ce1cee14887afef016e3a872487",
+            "0569b758ea9b4456b70248dcb4a92aff",
+            "e8537406dd7548ec8e5a8c9330ab7831",
+            "64257e8bc3fe4624b6841c6e896675e3",
+            "800cb944b9b94ae3a0fa2a48b8754fbf",
+            "a64b40f472b241a18cf640babda23642",
+            "237ea7c496b54ae78b3e52aac587dd0a"
+          ]
+        },
+        "id": "ZwXZbQ2dSwzI",
+        "outputId": "5d1b8b27-167a-44c3-ea41-2138050dac08"
+      },
+      "outputs": [
+        {
+          "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": "c7312aaf3b1042e49b2ce9ffcd458fe4"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
+        "import tensor_parallel as tp\n",
+        "\n",
+        "#model_name = \"codellama/CodeLlama-7b-hf\"\n",
+        "#model_name = \"codys12/MergeLlama-7b\"\n",
+        "model_name = \"NousResearch/Yarn-Mistral-7b-128k\"\n",
+        "#model_name = \"NousResearch/Yarn-Llama-2-7b-128k\"\n",
+        "\n",
+        "model = AutoModelForCausalLM.from_pretrained(\n",
+        "    model_name,\n",
+        "    trust_remote_code=True,\n",
+        "    torch_dtype=torch.float16,\n",
+        "    use_flash_attention_2=True,\n",
+        "    device_map='auto',\n",
+        ")\n",
+        "model.half()\n",
+        "\n",
+        "model.config.use_cache = False"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XDS2yYmlUAD6"
+      },
+      "outputs": [],
+      "source": [
+        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
+        "tokenizer.pad_token = tokenizer.eos_token\n",
+        "tokenizer.padding_side = \"left\"\n",
+        "\n",
+        "#special_tokens = ['<<<<<<<', '=======', '>>>>>>>']\n",
+        "\n",
+        "#tokenizer.add_special_tokens({'additional_special_tokens': special_tokens})\n",
+        "\n",
+        "#model.resize_token_embeddings(len(tokenizer))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dQdvjTYTT1vQ"
+      },
+      "outputs": [],
+      "source": [
+        "from peft import LoraConfig, get_peft_model\n",
+        "\n",
+        "lora_alpha = 16\n",
+        "lora_dropout = 0.1\n",
+        "lora_r = 16\n",
+        "\n",
+        "peft_config = LoraConfig(\n",
+        "    lora_alpha=lora_alpha,\n",
+        "    lora_dropout=lora_dropout,\n",
+        "    r=lora_r,\n",
+        "    bias=\"none\",\n",
+        "    task_type=\"CAUSAL_LM\",\n",
+        "    #modules_to_save = [\"lm_head\", \"embed_tokens\"]\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OCFTvGW6aspE"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import TrainingArguments\n",
+        "\n",
+        "output_dir = \"./results\"\n",
+        "per_device_train_batch_size = 3\n",
+        "gradient_accumulation_steps = 4\n",
+        "optim = \"paged_adamw_32bit\"\n",
+        "save_steps = 100\n",
+        "logging_steps = 10\n",
+        "learning_rate = 2e-4\n",
+        "max_grad_norm = 0.3\n",
+        "num_train_epochs = 1\n",
+        "warmup_ratio = 0.03\n",
+        "lr_scheduler_type = \"constant\"\n",
+        "\n",
+        "training_arguments = TrainingArguments(\n",
+        "    output_dir=output_dir,\n",
+        "    per_device_train_batch_size=per_device_train_batch_size,\n",
+        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
+        "    optim=optim,\n",
+        "    #save_steps=save_steps,\n",
+        "    save_strategy=\"epoch\",\n",
+        "    logging_steps=logging_steps,\n",
+        "    learning_rate=learning_rate,\n",
+        "    #fp16=False,\n",
+        "    max_grad_norm=max_grad_norm,\n",
+        "    num_train_epochs=num_train_epochs,\n",
+        "    warmup_ratio=warmup_ratio,\n",
+        "    group_by_length=False,\n",
+        "    lr_scheduler_type=lr_scheduler_type,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TNeOBgZeTl2H",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e5c37a9c-9a2c-4860-8174-4252e6883c12"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/trl/trainer/ppo_config.py:141: UserWarning: The `optimize_cuda_cache` arguement will be deprecated soon, please use `optimize_device_cache` instead.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:247: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "from trl import SFTTrainer, DataCollatorForCompletionOnlyLM\n",
+        "\n",
+        "max_seq_length = 768\n",
+        "\n",
+        "#model = tp.tensor_parallel(model)\n",
+        "# instruction_template = \"<<<<<<<\"\n",
+        "# response_template = \">>>>>>>\"\n",
+        "# collator = DataCollatorForCompletionOnlyLM(instruction_template=instruction_template, response_template=response_template, tokenizer=tokenizer, mlm=False)\n",
+        "\n",
+        "# def formatting_prompts_func(example):\n",
+        "#     output_texts = []\n",
+        "#     for i in range(len(example['conflict_resolution'])):\n",
+        "#         text = example['conflict_resolution'][i]\n",
+        "#         output_texts.append(text)\n",
+        "#     return output_texts\n",
+        "\n",
+        "trainer = SFTTrainer(\n",
+        "    model=model,\n",
+        "    train_dataset=full_dataset,\n",
+        "    #eval_dataset=test_dataset,\n",
+        "    peft_config=peft_config,\n",
+        "    dataset_text_field=\"text\",\n",
+        "    max_seq_length=max_seq_length,\n",
+        "    tokenizer=tokenizer,\n",
+        "    #data_collator=collator,\n",
+        "    #formatting_func=formatting_prompts_func,\n",
+        "    args=training_arguments,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7OyIvEx7b1GT"
+      },
+      "outputs": [],
+      "source": [
+        "for name, module in trainer.model.named_modules():\n",
+        "    if \"norm\" in name:\n",
+        "        module = module.to(torch.float32)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.half()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "E4Ns5ZyCwizG",
+        "outputId": "6ba9ac28-bb32-4adf-8cc4-3768bcca5ad8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "MistralForCausalLM(\n",
+              "  (model): MistralModel(\n",
+              "    (embed_tokens): Embedding(32000, 4096)\n",
+              "    (layers): ModuleList(\n",
+              "      (0-31): 32 x MistralDecoderLayer(\n",
+              "        (self_attn): MistralFlashAttention2(\n",
+              "          (q_proj): lora.Linear(\n",
+              "            (base_layer): Linear(in_features=4096, out_features=4096, bias=False)\n",
+              "            (lora_dropout): ModuleDict(\n",
+              "              (default): Dropout(p=0.1, inplace=False)\n",
+              "            )\n",
+              "            (lora_A): ModuleDict(\n",
+              "              (default): Linear(in_features=4096, out_features=16, bias=False)\n",
+              "            )\n",
+              "            (lora_B): ModuleDict(\n",
+              "              (default): Linear(in_features=16, out_features=4096, bias=False)\n",
+              "            )\n",
+              "            (lora_embedding_A): ParameterDict()\n",
+              "            (lora_embedding_B): ParameterDict()\n",
+              "          )\n",
+              "          (k_proj): Linear(in_features=4096, out_features=1024, bias=False)\n",
+              "          (v_proj): lora.Linear(\n",
+              "            (base_layer): Linear(in_features=4096, out_features=1024, bias=False)\n",
+              "            (lora_dropout): ModuleDict(\n",
+              "              (default): Dropout(p=0.1, inplace=False)\n",
+              "            )\n",
+              "            (lora_A): ModuleDict(\n",
+              "              (default): Linear(in_features=4096, out_features=16, bias=False)\n",
+              "            )\n",
+              "            (lora_B): ModuleDict(\n",
+              "              (default): Linear(in_features=16, out_features=1024, bias=False)\n",
+              "            )\n",
+              "            (lora_embedding_A): ParameterDict()\n",
+              "            (lora_embedding_B): ParameterDict()\n",
+              "          )\n",
+              "          (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
+              "          (rotary_emb): MistralYaRNScaledRotaryEmbedding()\n",
+              "        )\n",
+              "        (mlp): MistralMLP(\n",
+              "          (gate_proj): Linear(in_features=4096, out_features=14336, bias=False)\n",
+              "          (up_proj): Linear(in_features=4096, out_features=14336, bias=False)\n",
+              "          (down_proj): Linear(in_features=14336, out_features=4096, bias=False)\n",
+              "          (act_fn): SiLUActivation()\n",
+              "        )\n",
+              "        (input_layernorm): MistralRMSNorm()\n",
+              "        (post_attention_layernorm): MistralRMSNorm()\n",
+              "      )\n",
+              "    )\n",
+              "    (norm): MistralRMSNorm()\n",
+              "  )\n",
+              "  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)\n",
+              ")"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1JApkSrCcL3O"
+      },
+      "source": [
+        "## Train the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 770
+        },
+        "id": "_kbS7nRxcMt7",
+        "outputId": "b162549b-a5db-4557-eb0a-f8857cb05f3a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mcodysteinmetz7\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Tracking run with wandb version 0.16.0"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Run data is saved locally in <code>/content/wandb/run-20231116_171138-g4peof8l</code>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Syncing run <strong><a href='https://wandb.ai/codysteinmetz7/huggingface/runs/g4peof8l' target=\"_blank\">curious-firebrand-68</a></strong> to <a href='https://wandb.ai/codysteinmetz7/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              " View project at <a href='https://wandb.ai/codysteinmetz7/huggingface' target=\"_blank\">https://wandb.ai/codysteinmetz7/huggingface</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              " View run at <a href='https://wandb.ai/codysteinmetz7/huggingface/runs/g4peof8l' target=\"_blank\">https://wandb.ai/codysteinmetz7/huggingface/runs/g4peof8l</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "You're using a LlamaTokenizerFast 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='166' max='166' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [166/166 03:56, 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>10</td>\n",
+              "      <td>1.562600</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>20</td>\n",
+              "      <td>1.144800</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>30</td>\n",
+              "      <td>1.091400</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>40</td>\n",
+              "      <td>1.005600</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>50</td>\n",
+              "      <td>0.973000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>60</td>\n",
+              "      <td>0.945800</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>70</td>\n",
+              "      <td>0.964000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>80</td>\n",
+              "      <td>0.933100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>90</td>\n",
+              "      <td>0.946800</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>100</td>\n",
+              "      <td>0.893600</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>110</td>\n",
+              "      <td>0.930500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>120</td>\n",
+              "      <td>0.935700</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>130</td>\n",
+              "      <td>0.916900</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>140</td>\n",
+              "      <td>0.969000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>150</td>\n",
+              "      <td>0.912600</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>160</td>\n",
+              "      <td>0.928400</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "TrainOutput(global_step=166, training_loss=1.0009092394127903, metrics={'train_runtime': 243.7045, 'train_samples_per_second': 8.207, 'train_steps_per_second': 0.681, 'total_flos': 4.667931048886272e+16, 'train_loss': 1.0009092394127903, 'epoch': 1.0})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
+      "source": [
+        "trainer.train()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "d2a42eef2730409ebf393b436e9b1cb2",
+            "1b15326622c846d890d9cc46f2b9e910",
+            "0c7137bece264cd6aecef180d67c2594",
+            "68399db4e45b45ebae3089f446536fe6",
+            "c31967bae9ed4fedb0236e8e12e179ff",
+            "a73387128ad94130ba42c47ebc305dd7",
+            "588f343ef4e44aa7b0d3b7947999ec2f",
+            "d09ed21833894b2392c82623590cceca",
+            "c11a6994dbe44b619c70c31b36e11124",
+            "2ddaf90573204459b664e74366bc5b24",
+            "e80fa5ade2994beab98967b60d9bfaef",
+            "12c7dce184514bc594a21bf3c0c3bb40",
+            "a7e1e3bdd1b14302b47bc6ca32a1d0b7",
+            "12c6f2e3e50c4f50ac04df01e0d5ea48",
+            "196c9b90353c486d81f35808f3a596ac",
+            "e9543a04f3c74b3a96e8e83f0553297b",
+            "a5ac8c334dd44452bf8ce7226a83e78f",
+            "0492dcbe2fc149d790b431195b3b2679",
+            "508ebe0aa0344f88be890c7f1fa4f78a",
+            "0e186430f4a847cba212f61da4858721",
+            "405c6ef86acc4086807a09b5f9268b3e",
+            "2e6fe275e4214f67a0077ecf2b9754df",
+            "0d19059f1e3a4a12b6cdc311be8756aa"
+          ]
+        },
+        "id": "niyf5_Kc4ugO",
+        "outputId": "03076f8c-199e-45b0-e8e7-a4792d1af4fd"
+      },
+      "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": "d2a42eef2730409ebf393b436e9b1cb2"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from huggingface_hub import login\n",
+        "login()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 101,
+          "referenced_widgets": [
+            "eda1a0873d164b1694ef2a90ca5d693f",
+            "3f5538cacd0941e5a7dbe222ceded839",
+            "4063bb00683e4b5a88b529aac0c7efa7",
+            "b56bb08e1d094f2e836b50ac4691030e",
+            "52a2f49016e74b498703505011663456",
+            "2148cec4f474400eac3395d7ac37c82f",
+            "5a42a39acb2f42a6a30fd726ae671ac9",
+            "4c563351f1d24532a4faeaebb4f49186",
+            "c2b172657edf4e24ac546a330e30f52f",
+            "c2fc136fd44d4f0c81937afd5ab708aa",
+            "f8b79a63c90d485e8a8188da348ef75f"
+          ]
+        },
+        "id": "v2CEgCF14M0m",
+        "outputId": "e3dce87a-91fb-4550-88c2-2d2ff998b325"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "adapter_model.safetensors:   0%|          | 0.00/13.6M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "eda1a0873d164b1694ef2a90ca5d693f"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "CommitInfo(commit_url='https://huggingface.co/codys12/Mistral-7b-Pathway-128k-4/commit/8bb8e83a7b36b3eb814c19fb2350be91bfa3ab9a', commit_message='Upload model', commit_description='', oid='8bb8e83a7b36b3eb814c19fb2350be91bfa3ab9a', pr_url=None, pr_revision=None, pr_num=None)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ],
+      "source": [
+        "model_to_save = trainer.model.module if hasattr(trainer.model, 'module') else trainer.model  # Take care of distributed/parallel training\n",
+        "model_to_save.save_pretrained(\"outputs\")\n",
+        "model_to_save.push_to_hub(\"Mistral-7b-Pathway-128k-4\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_to_save = trainer.model.module if hasattr(trainer.model, 'module') else trainer.model  # Take care of distributed/parallel training\n"
+      ],
+      "metadata": {
+        "id": "jCvm4X9HUfZl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qmA4G6C64dJ4"
+      },
+      "outputs": [],
+      "source": [
+        "lora_config = LoraConfig.from_pretrained('outputs')\n",
+        "model2 = get_peft_model(model, lora_config)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pgt86z-x4diG",
+        "outputId": "1bcd39bf-1913-4342-b58e-0e096e19a210"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
+          ]
+        }
+      ],
+      "source": [
+        "text = \"<<SYS>> Instruction: Determine if the user plans to attend the upcoming community meeting.\\nConclusions: \\nThe user plans to attend: [ATTENDING],\\nThe user does not plan to attend: [NOT ATTENDING],\\nUser is unsure about attending: [UNSURE]\\nContext: The community meeting is scheduled for June 15th. [1]\\n\\n<</SYS>><s>Will you be coming to the meeting on June 15th?[1]</s><s>[INST]\"\n",
+        "device = \"cuda:0\"\n",
+        "\n",
+        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
+        "outputs = model_to_save.generate(**inputs, max_new_tokens=400)\n",
+        "\n",
+        "print(tokenizer.decode(outputs[0], skip_special_tokens=False))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fjOInz-Q-54k",
+        "outputId": "9cdd6682-958c-4369-e96c-e82c614979b8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "CommitInfo(commit_url='https://huggingface.co/codys12/MergeLlama-7b/commit/6de5455897d4d320e0cde4e428cf4b32e2430222', commit_message='Upload tokenizer', commit_description='', oid='6de5455897d4d320e0cde4e428cf4b32e2430222', pr_url=None, pr_revision=None, pr_num=None)"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "new_model_name = \"Pathway-test\"\n",
+        "model2.push_to_hub(new_model_name)\n",
+        "tokenizer.push_to_hub(new_model_name)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "machine_shape": "hm",
+      "provenance": [],
+      "gpuType": "A100"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "c7312aaf3b1042e49b2ce9ffcd458fe4": {
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+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
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+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
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+            "children": [
+              "IPY_MODEL_16305f29346e4458be44270922757d82",
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+}