{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Poetry Fusion using Llama 3.2\n", "\n", "This notebook demonstrates the fine-tuning of the Llama 3.2 model to generate poetry that fuses \n", "the styles of multiple poets. We will apply full fine-tuning, prompt engineering, and Parameter \n", "Efficient Fine-Tuning (PEFT) to maximize model performance and creativity in poetry generation.\n", "\n", "### Major Steps\n", "1. **Setup**: Installing necessary libraries and initializing the environment.\n", "2. **Dataset Preparation**: Loading and preprocessing the dataset.\n", "3. **PEFT (Parameter Efficient Fine-Tuning)**: Applying PEFT techniques to optimize model performance and efficiency.\n", "4. **Training**: Fine-tuning the model to capture stylistic characteristics of selected poets.\n", "5. **Prompt Engineering**: Crafting prompts to guide the model in fusing poetic styles effectively.\n", "\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mNnkgBq7Q3EU", "outputId": "1f3b2038-0cae-48df-dd69-09367b779453" }, "outputs": [ { "name": "stdout", "output_type": "stream", "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" ] } ], "source": [ "!pip install -q -U trl transformers accelerate git+https://github.com/huggingface/peft.git\n", "!pip install -q datasets bitsandbytes einops wandb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Dataset Preparation\n", "\n", "We will use a selected dataset that includes various poet styles. This dataset will be preprocessed \n", "to highlight stylistic characteristics for model fine-tuning, focusing on continuous and meaningful \n", "poetic text for optimal style fusion.\n", "\n", "*Note: Ensure the dataset is accessible and formatted to match model input requirements.*\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0X3kHnskSWU4", "outputId": "551e0c29-d0e6-48b9-a92e-18280e331088" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset({\n", " features: ['Title', 'Poem', 'Poet', 'Emotion'],\n", " num_rows: 524\n", "})\n" ] } ], "source": [ "from datasets import load_dataset\n", "\n", "# Specify the path to your CSV file\n", "csv_file_path = \"/YourPathToFile/LLM_Dataset.csv\"\n", "\n", "# Load the dataset from the CSV file\n", "dataset = load_dataset(\"csv\", data_files=csv_file_path, split=\"train\")\n", "\n", "# Inspect the first few rows of the dataset\n", "print(dataset)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "3ln1QrYKCwlI" }, "outputs": [], "source": [ "def preprocess_function(examples):\n", " # Assuming 'Poet', 'Emotion', and 'Poem' columns exist\n", " # Create a new 'fusion_text' column with author and emotion information\n", " examples['fusion_text'] = [\n", " f\"Author: {author}, Emotion: {emotion}\\n{poem}\"\n", " for author, emotion, poem in zip(examples['Poet'], examples['Emotion'], examples['Poem'])\n", " ]\n", " return examples\n", "\n", "dataset = dataset.map(preprocess_function, batched=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "rjOMoSbGSxx9" }, "source": [ "## Loading the model" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZwXZbQ2dSwzI", "outputId": "d5f18699-91a2-4546-b2ff-f85e94853af3" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n" ] } ], "source": [ "import torch\n", "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n", "\n", "from huggingface_hub import login\n", "\n", "# Login to Hugging Face\n", "login('HereInsertYourHuggingFaceAPIKey')\n", "\n", "model_name = \"meta-llama/Llama-3.2-1B-Instruct\"\n", "\n", "bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True,\n", " bnb_4bit_quant_type=\"nf4\",\n", " bnb_4bit_compute_dtype=torch.float16,\n", ")\n", "\n", "model = AutoModelForCausalLM.from_pretrained(\n", " model_name,\n", " quantization_config=bnb_config,\n", " trust_remote_code=True\n", ")\n", "model.config.use_cache = False" ] }, { "cell_type": "markdown", "metadata": { "id": "xNqIYtQcUBSm" }, "source": [ "Let's also load the tokenizer below" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 133, "referenced_widgets": [ "1688bda48df74ff2a5d377823912d6b6", "122b1a9b0d5d4d93ac9e5c2edc22c0ee", "9ea70b72ab8245f2a10be81481a347da", "948566c079df42678d08d59b9900fb1d", "20de05cfdd5142059078216ab6160cd4", "4c71c1a961874936b18522c7241d97cc", "668ba96e644c4f62a2faab36e67b50eb", "3d1757f3cb9d47e0860f3fa27e093177", "2cb5372736e1444c9633617b9c0bd3b5", "6608c011f2354593b79fa7db53908cb7", "8985097ded454d43a58bc7d5a421efb6", "1d7f4142f7f348549f5a215fed5cf454", "e52103e58c0f44aaa1f97f1a7a75d762", "574bd1d2794b43ba80ceb6db3aa342eb", "503aa9964ac346efab3fcd896815c00d", "86fb7f3d35c8411687b391f7b484a099", "1c2bf57f7afc44b09b96b81b1e4c9241", "3e28c4291521438a84d14d6eb295a580", "dfab1b9fbaaa43c1880168da723bd2ed", "be828e16914f447aa81e3bd041c0e7c1", "0bd59374c07446fd8fbce159e756662d", "5b58a1e445374450b8093f6383d40fba", "9283d9bacd1b422b972979feed2d3b71", "83bc630ca0db4b458c7f7005f2f4de52", "0d0615f842d1470db73d546c6da39c5a", "fbd9096a63a343fd9543b940401ac6d9", "6ad39cd2af5c4aeab1a530b47afe776b", "9d7e03502609450e85b4272ca0a09801", "c76b96659cf2440f880c43ab55146268", "b6b802d4b89a4bb8872831499fa10bbc", "c3845b5d2a204b6798a84a0d580d635e", "dac608458fca497aa485664f231ef530", "50266bc539a8415c8ebca9eb4edbf58f" ] }, "id": "XDS2yYmlUAD6", "outputId": "eee722b6-6a1a-4b95-a6ba-609a56a5f79a" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1688bda48df74ff2a5d377823912d6b6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "tokenizer_config.json: 0%| | 0.00/54.5k [00:00 {\n function loadScript(url) {\n return new Promise(function(resolve, reject) {\n let newScript = document.createElement(\"script\");\n newScript.onerror = reject;\n newScript.onload = resolve;\n document.body.appendChild(newScript);\n newScript.src = url;\n });\n }\n loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n const iframe = document.createElement('iframe')\n iframe.style.cssText = \"width:0;height:0;border:none\"\n document.body.appendChild(iframe)\n const handshake = new Postmate({\n container: iframe,\n url: 'https://wandb.ai/authorize'\n });\n const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n handshake.then(function(child) {\n child.on('authorize', data => {\n clearTimeout(timeout)\n resolve(data)\n });\n });\n })\n });\n ", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n", "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n", "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:" ] }, { "name": "stdout", "output_type": "stream", "text": [ " ··········\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.18.5" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /content/wandb/run-20241101_122229-qer14j89" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run ./results to Weights & Biases (docs)
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StepTraining Loss
104.638000
204.891500
304.571500
404.801100
504.902100
604.506600
704.830300
804.391100
904.623500
1004.581300
1104.320500
1204.469800
1304.230400
1404.406000
1504.455000
1604.190500
1704.370600
1804.359100
1904.130300
2004.405300
2104.084100
2204.377600
2304.387700
2404.130100
2504.279500
2604.089200
2704.370800
2804.335400
2904.120900
3004.234700
3104.097200
3204.322700
3304.249700
3404.075000
3504.330600
3604.228500
3704.123200
3804.256200
3904.031300
4004.262200
4104.344200
4204.036900
4304.203000
4404.020200
4504.323000
4604.245400
4704.007300
4804.292800
4903.941300
5004.207400
5104.274500
5203.960800
5304.186600
5403.937100
5504.340500
5604.150400
5703.978500
5804.203600
5904.211500
6003.926900
6104.209500
6203.942000
6304.206900
6404.214200
6503.940100
6604.077100
6703.947800
6804.170700
6904.211300
7003.928600
7104.118900
7203.961000
7304.187800
7404.207500
7503.920400
7604.166200
7704.123700
7803.892500
7904.161300
8003.885900
8104.137200
8204.194000
8303.908900
8404.088500
8503.892800
8604.148800
8704.169400
8803.822300
8904.130200
9003.881700
9104.168400
9204.032900
9303.859800
9404.157000
9504.099800
9603.868900
9704.091500
9803.904500
9904.155600
10004.018200
10103.881900
10204.037200
10303.868000
10404.083800
10504.051600
10603.795500
10704.124000
10803.882300
10904.111300
11003.950100
11103.886600
11204.060200
11304.030100
11403.828800
11504.091500
11603.791500
11704.142900
11803.998800
11903.809700
12004.075500
12103.838900
12204.024400
12304.052700
12403.782700
12504.122700
12603.823200
12703.916000
12804.039000
12903.839700
13004.040200
13103.943800
13203.840800
13304.034000
13403.809200
13503.985400
13604.050400
13703.755700
13804.088900
13903.766700
14003.968300
14104.047700
14203.780700
14304.077600
14403.781000
14503.945000
14604.047900
14703.790200
14803.996400
14904.004800
15003.811700
15104.055500
15203.715500
15303.998200
15404.012000
15503.759600
15604.033500
15703.723500
15804.024300
15904.018600
16003.765500
16104.016700
16203.694500
16303.999000
16404.061300
16503.715400
16603.960900
16703.944800
16803.855700
16904.054800
17003.742600
17103.969800
17204.022800
17303.785900
17403.919300
17503.767200
17604.006700
17703.978900
17803.819900
17903.913600
18003.771800
18103.979500
18204.018900
18303.712100
18404.067400
18503.661800
18604.025000
18704.033300
18803.753600
18903.953000
19003.992500
19103.737400
19203.964900
19303.781300
19404.055700
19503.911200
19603.768000
19703.954300
19803.727800
19904.042600
20003.977200

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "TrainOutput(global_step=2000, training_loss=4.060946846008301, metrics={'train_runtime': 8576.9213, 'train_samples_per_second': 7.462, 'train_steps_per_second': 0.233, 'total_flos': 9.1423220805845e+16, 'train_loss': 4.060946846008301, 'epoch': 122.13740458015268})" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.train()" ] }, { "cell_type": "markdown", "metadata": { "id": "H5c0ppfasK29" }, "source": [ "The `SFTTrainer` also takes care of properly saving only the adapters during training instead of saving the entire model." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "v2CEgCF14M0m" }, "outputs": [], "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\")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "id": "qmA4G6C64dJ4" }, "outputs": [], "source": [ "lora_config = LoraConfig.from_pretrained('outputs')\n", "model = get_peft_model(model, lora_config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Afer training and saving the trained model, we may focus on the prompt engeneering." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pgt86z-x4diG", "outputId": "3aceeb17-6197-4c01-81d1-2881bca59bb6" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "You are an expert in poetry fusion, specializing in blending the distinct styles of two poets. Focus on emotional depth, unique metaphor usage, symbolic imagery, and rhythmic patterns. Your task is to merge not only technical elements like word choice and structure but also the deeper conceptual and emotional richness that define each poet's work.\n", "\n", "User: \n", "Generate a new poem that fuses the styles of William Shakespeare and Edgar Allan Poe. Combine their styles in the rest of the poem, merging their use of metaphor, rhythm, and tone. Make the poem to 150 words.\n", "\n", "Assistant: \n", "I'd be delighted to create a fusion poem for you. Here's my attempt:\n", "\n", "\"In twilight's hush, where shadows dance and play,\n", "Amidst the whispers of a dying day,\n", "The moon, a ghostly galleon, sails the night,\n", "With stars that weep and wail, in sorrow's plight.\n", "The trees, like skeletal hands, reach for the sky,\n", "Their branches etched with sorrow, as the wind whispers by.\n", "In this macabre waltz, where darkness reigns,\n", "I find myself lost, in a labyrinth of pain.\n", "Yet, in this eerie dance, I find a strange delight,\n", "A morbid charm that beckons me to take flight.\n", "For in the shadows, I discover a truth profound,\n", "That love and death entwine, like the threads of a rope profound.\n", "And so, I'll dance with darkness, and find my way,\n", "Through the labyrinth of sorrow, to a brighter day.\"\n", "\n", "In this fusion poem, I've tried to combine the technical elements of Shakespeare's style (e.g., iambic pentameter, metaphorical language) with the symbolic imagery and emotional depth of Poe's work. The result is a poem that is both a tribute to the original poets and a unique creation\n" ] } ], "source": [ "system_message = \"\"\"\n", "You are an expert in poetry fusion, specializing in blending the distinct styles of two poets. Focus on emotional depth, unique metaphor usage, symbolic imagery, and rhythmic patterns. Your task is to merge not only technical elements like word choice and structure but also the deeper conceptual and emotional richness that define each poet's work.\n", "\"\"\"\n", "\n", "Poet_1, Poet_2 = \"William Shakespeare\", \"Edgar Allan Poe\"\n", "user_message = f\"\"\"\n", "Generate a new poem that fuses the styles of {Poet_1} and {Poet_2}. Combine their styles in the rest of the poem, merging their use of metaphor, rhythm, and tone. Make the poem to 150 words.\n", "\"\"\"\n", "\n", "prompt = f\"{system_message}\\nUser: {user_message}\\nAssistant:\"\n", "\n", "\n", "device = \"cuda:0\"\n", "\n", "# Custom decoding with temperature, top_p, and top_k for more creative output\n", "def generate_with_constraints(prompt, temperature=0.7, top_p=0.9, top_k=50):\n", " inputs = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda:0\")\n", " outputs = model.generate(\n", " **inputs,\n", " max_new_tokens=256,\n", " temperature=temperature, # Added temperature for creativity\n", " top_p=top_p, # Nucleus sampling\n", " top_k=top_k, # Limit token selection for more focused choices\n", " no_repeat_ngram_size=3,\n", " num_beams=5 # Beam search to enforce rhyme/meter constraints\n", " )\n", " return tokenizer.decode(outputs[0], skip_special_tokens=True)\n", "\n", "# Now generate the poem with creativity-enhancing parameters\n", "poetry_output = generate_with_constraints(prompt, temperature=0.8, top_p=0.85, top_k=40)\n", 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