{ "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 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"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>>>>>>']\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": [ "" ], "text/html": [ "Tracking run with wandb version 0.16.0" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "Run data is saved locally in /content/wandb/run-20231116_171138-g4peof8l" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "Syncing run curious-firebrand-68 to Weights & Biases (docs)
" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ " View project at https://wandb.ai/codysteinmetz7/huggingface" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ " View run at https://wandb.ai/codysteinmetz7/huggingface/runs/g4peof8l" ] }, "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": [ "" ], "text/html": [ "\n", "
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StepTraining Loss
101.562600
201.144800
301.091400
401.005600
500.973000
600.945800
700.964000
800.933100
900.946800
1000.893600
1100.930500
1200.935700
1300.916900
1400.969000
1500.912600
1600.928400

" ] }, "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='

> 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<>Will you be coming to the meeting on June 15th?[1][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", 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Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
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