diff --git "a/llama_3b.ipynb" "b/llama_3b.ipynb" new file mode 100644--- /dev/null +++ "b/llama_3b.ipynb" @@ -0,0 +1,3023 @@ +{ + "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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= 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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" + ], + "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": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "010b87ea4a7f4fe0a3bee6fb9ea5808c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "012be4059402403d879f4c21282d01f3": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", 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