{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "w7acpG9P0xz5", "outputId": "d43b52f9-00a5-4785-a808-468bd9df0064" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import torch\n", "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "CeKFKq622Rdf", "outputId": "d99ed2ff-aa27-4e26-cadc-390f1736da25" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "
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00Title: Iteration Counter for Mandelbrot Fracta...
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\n" ], "text/plain": [ " Unnamed: 0 text\n", "0 0 Title: Iteration Counter for Mandelbrot Fracta...\n", "1 1 Design Problem Statement:\\n\\nDesign a multiple...\n", "2 2 Problem Statement:\\n\\nDesign a Data Memory mod...\n", "3 3 Problem Statement:\\n\\nDesign and implement a h...\n", "4 4 Problem Statement:\\n\\nDesign a module named \"c..." ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_final = pd.read_csv('/content/drive/MyDrive/Llama_Project/df_final.csv')\n", "df_final.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "240b477bcb00403d93dcdd8b6f62a9db", "2f680cafc2cc4e55a688e48b0829f414", "0e48c48db5aa4d0ebd6041e907f3f50a", "b0a44032a5c94e52988081c53f665df6", "6c758eef80194350adbf9ace376aa498", "4b82eef4ec814a988a80e6ae869bbdbe", "cb03668b83a4443abae9c190b4030c4d", "51bf435dd3d14430801fee773ca0f57a", "ff3fb98c62c746d08f70e4b2bd086939", "50d242ffa45d4e6a85ee93e59f74110c", "a2d15237c75242b98104751b1de05256", "123ea8655f2e4b7386757e1ee392721c", "6ca40832a85d47b7addbf73f40c87d1f", "f83fd8afe6c04c8db57d6a70c7126859", "3a099f570c4946018e486e2181ca105a", "24622a331b6241fa8669adcabb7e2fa4", "71fa599b33dd444e91d68fd8b991781b", "567b1fea3b2f49a1b9dfc70527f8fe23", "c73e7ca25c844171b6a18e67efd22ca0", "288ba841d43f47fbafcd4d2aca24f4f7", "fc9ea936a4f04d0caf5ad35bf9e1fad1", "80c26947f000425f940d0a345fa8781e", "c94297cb02f645458924d931406072cd", "7757fb969f9841e4aa976abdde9b5e0f", "dc4b6b83a14a40be98a0fd20473bf170", "80c922d63cc24a0981f23c074a3131fa", "012be4059402403d879f4c21282d01f3", "010b87ea4a7f4fe0a3bee6fb9ea5808c", "2d5b58b18d2e4260901ab78a48e4046f", "47501a292ba245489d6148528320bad5", "595515f8eee24a70a3cc71958e233913", "640207781c914a0ca690e00437382913", "8e3ad32c145e4d7ca2def6755bbc0d7a", "5362423fed074a6ba61970adf96ecda6", "91ebedbfa4454d0cb8431abdc3361a65", "7359baa29afd46f18fae2c8b8f82cd73", "41f3916a04104bf6900c097fd66b7c57", "864a9a610e8544969fb5ddab829d41cd", "5790f36875d34449b53e51f36434111c", "245518ddc86c453682ae9d5ee0a2dc3a", "05e65000f83a483c80738b23f1be4c7e", "521bee4b39b740439cbd680c1b196e80", "419c9be2e9bc44d9876f2eb23f35f1d2", "62965c8dd60a46f19ffa7045e7dbed8b", "02548c49cb5c410995f03d4ff373ede4", "c92d49568abb4946a5cf334d5d37b7c2", "6423baf45a58428c8bb3c32650c3ccab", "1d5ba0fec6bf4b54a6f72a56b719aaf9", "ea5e45910a1a4caca9987bc576345f22", "df3ead74de95497bb784d11661d04f89", "9d4989be92544d66921531e346e2345c", "9cea73371974465680e5552d788159f5", "c522e913d96f43d395d3f1524cf630d1", "2f48640ce89047e984e44b17be2abbce", "238ea301069142d189d50b6d82efe195", "31955cbad0be4a2cbe1a4c05776a4f68", "026e96e6cce940e58ecbd37e8fca1fc0", "bb7f80e6807b40bbaee574ec6e400de2", "b6fb61101588424e97424e4bc7ab7268" ] }, "id": "eRGkWE3g2Rb6", "outputId": "17d851e6-17ed-403a-c2cc-d1dcfecc4505" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "240b477bcb00403d93dcdd8b6f62a9db", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
= block_size:\n", "# total_length = (total_length // block_size) * block_size\n", "# result = {\n", "# k: [t[i : i + block_size] for i in range(0, total_length, block_size)]\n", "# for k, t in inputs.items()\n", "# }\n", "# result[\"labels\"] = result[\"input_ids\"].copy()\n", "# return result\n", "\n", "# lm_dataset = tokenized_dataset.map(group_texts, batched=True)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "id": "fJHSH3ZzQlsA" }, "outputs": [], "source": [ "from transformers import TrainingArguments\n", "!pip install -q trl\n", "from trl import SFTTrainer\n", "from peft import LoraConfig, AutoPeftModelForCausalLM\n", "\n", "num_train_epochs = 1\n", "fp16 = False\n", "bf16 = False\n", "per_device_train_batch_size = 4\n", "gradient_accumulation_steps = 1\n", "gradient_checkpointing = True\n", "max_grad_norm = 0.3\n", "learning_rate = 2e-4\n", "weight_decay = 0.001\n", "optim = \"paged_adamw_32bit\"\n", "lr_scheduler_type = \"cosine\"\n", "warmup_ratio = 0.03\n", "group_by_length = False\n", "save_steps = 0\n", "logging_steps = 25\n", "disable_tqdm= False\n", "output_dir = '\\content\\drive\\MyDrive\\LLM\\llama-7b'\n", "packing = True\n", "max_seq_length = 1024\n", "hf_model_repo = \"llama-3b-hdl\"\n", "\n", "training_args = TrainingArguments(\n", " output_dir=output_dir,\n", " num_train_epochs=num_train_epochs,\n", " per_device_train_batch_size=per_device_train_batch_size,\n", " gradient_accumulation_steps=gradient_accumulation_steps,\n", " gradient_checkpointing=gradient_checkpointing,\n", " optim=optim,\n", " logging_steps=logging_steps,\n", " save_strategy=\"epoch\",\n", " learning_rate=learning_rate,\n", " weight_decay=weight_decay,\n", " fp16=fp16,\n", " bf16=bf16,\n", " max_grad_norm=max_grad_norm,\n", " warmup_ratio=warmup_ratio,\n", " group_by_length=group_by_length,\n", " lr_scheduler_type=lr_scheduler_type,\n", " disable_tqdm=disable_tqdm,\n", " report_to=\"tensorboard\",\n", " seed=224\n", ")\n", "\n", "peft_config = LoraConfig(lora_alpha=lora_alpha,\n", " lora_dropout=lora_dropout,\n", " r=lora_r,\n", " bias=\"none\",\n", " task_type=\"CAUSAL_LM\")\n", "\n", "def format_instruction(sample):\n", "\treturn f\"\"\"### Instruction:{sample}\"\"\"\n", "\n", "trainer = SFTTrainer(model=model,\n", " train_dataset=lm_dataset,\n", " peft_config=peft_config,\n", " tokenizer=tokenizer,\n", " max_seq_length=max_seq_length,\n", " packing=packing,\n", " args=training_args,\n", " formatting_func=format_instruction)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "VKkrT-p_QlqM", "outputId": "1ba94bd6-6924-4956-90e7-2ba77d4e02a9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Token indices sequence length is longer than the specified maximum sequence length for this model (9169 > 2048). Running this sequence through the model will result in indexing errors\n", "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" ] }, { "data": { "text/html": [ "\n", "
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StepTraining Loss
250.672800
500.733900
750.647500
1000.586300
1250.555100
1500.580100
1750.518100
2000.495700
2250.517600
2500.489800
2750.529000
3000.406400
3250.478800
3500.464300
3750.473000
4000.398400
4250.455500
4500.441900
4750.472400
5000.418800
5250.447100
5500.388000
5750.392800
6000.392200
6250.420100
6500.421000
6750.428600
7000.409400
7250.371300
7500.414000
7750.401800
8000.448500
8250.408400

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trainer.train()\n", "#model.push_to_hub(\"llama-trained\")" ] }, { "cell_type": "markdown", "metadata": { "id": "GfnqyQdFkYMB" }, "source": [ "Model training stopped after training for 5 hrs 43 mins due to runtime expairy. It will need to be trained an additional 7 hours to complete the training" ] }, { "cell_type": "markdown", "metadata": { "id": "QKt6WSGEXQp2" }, "source": [ "Merging base model with adapter weights" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "W0MCH6VCWKLL" }, "outputs": [], "source": [ "model = AutoPeftModelForCausalLM.from_pretrained(\n", " args.output_dir,\n", " low_cpu_mem_usage=True,\n", " return_dict=True,\n", " torch_dtype=torch.float16,\n", ")\n", "\n", "merged_model = model.merge_and_unload()\n", "\n", "merged_model.save_pretrained(\"merged_model\",safe_serialization=True)\n", "tokenizer.save_pretrained(\"merged_model\")\n", "merged_model.push_to_hub(hf_model_repo)\n", "tokenizer.push_to_hub(hf_model_repo)" ] }, { "cell_type": "markdown", "metadata": { "id": "xP3jtUvgYTg2" }, "source": [ "Inference Testing" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Raf2c1xdJFMq" }, "outputs": [], "source": [ "model = AutoModelForCausalLM.from_pretrained(hf_model_repo,\n", " load_in_4bit=True,\n", " torch_dtype=torch.float16,)\n", "prompt = \" \"\n", "input = tokenizer(prompt, return_tensor=\"pt\", truncation=True).input_ids\n", "output = model.generate(input_ids=input, max_new_tokens=1000, do_sample=True, top_p=0.9,temperature=0.9)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Xov7ZKuK5AbB" }, "outputs": [], "source": [ "# from transformers import DataCollatorForLanguageModeling\n", "\n", "# tokenizer.pad_token = tokenizer.eos_token\n", "# data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wgsW4IB25AZL" }, "outputs": [], "source": [ "# !pip install -q -U accelerate\n", "# !pip install -q -U transformers" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JZ5SUeD-43ib" }, "outputs": [], "source": [ "# from transformers import TrainingArguments, Trainer\n", "\n", "# training_args = TrainingArguments(output_dir='/content/drive/MyDrive/Llama_Project/gpt2-large-hdl',\n", "# per_device_train_batch_size=1,\n", "# evaluation_strategy=\"epoch\",\n", "# learning_rate=2e-5,\n", "# weight_decay=0.01,\n", "# push_to_hub=True,\n", "# )\n", "\n", "# trainer = Trainer(model=model,\n", "# args=training_args,\n", "# train_dataset=tokenized_dataset['train'],\n", "# eval_dataset=tokenized_dataset['test'],\n", "# data_collator=data_collator,\n", "# )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yftIZrYQ43gq" }, "outputs": [], "source": [ "# torch.cuda.empty_cache()\n", "# trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CyO1g7Aw53_0" }, "outputs": [], "source": [ "# model.push_to_hub('gpt2-large_hdl')\n", "# tokenizer.push_to_hub('gpt2-large_hdl')" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": 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