notebooks/train_llama3.1_8b.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IqM-T1RTzY6C"
   },
   "source": [
    "*This notebook is adapted from the [Unsloth LLaMA 3.1 8B Training Notebook](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) tutorial. Go support them!*\n",
    "\n",
    "<div class=\"align-center\">\n",
    "  <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
    "  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
    "  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a><br>Join Discord if you need help + ⭐ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐\n",
    "</div>\n",
    "\n",
    "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2eSvM9zX_2d3"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
    "apt-get install build-essential -y\n",
    "pip install huggingface_hub wandb\n",
    "pip uninstall torch -y\n",
    "pip install torch==2.3.0\n",
    "pip install -U optimum\n",
    "pip uninstall xformers -y\n",
    "pip install -U xformers --index-url https://download.pytorch.org/whl/cu121\n",
    "pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
    "pip install --no-deps xformers \"trl<0.9.0\" peft accelerate bitsandbytes\n",
    "wandb login <YOUR_WANDB_API_KEY>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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    },
    "id": "QmUBVEnvCDJv",
    "outputId": "72355850-0409-442f-a58a-b3a9c4dfc99d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
      "==((====))==  Unsloth: Fast Llama patching release 2024.7\n",
      "   \\\\   /|    GPU: NVIDIA GeForce RTX 3090. Max memory: 23.691 GB. Platform = Linux.\n",
      "O^O/ \\_/ \\    Pytorch: 2.3.0+cu121. CUDA = 8.6. CUDA Toolkit = 12.1.\n",
      "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.26.post1. FA2 = False]\n",
      " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n"
     ]
    }
   ],
   "source": [
    "from unsloth import FastLanguageModel\n",
    "import torch\n",
    "\n",
    "max_seq_length = 1248 # Choose any! We auto support RoPE Scaling internally!\n",
    "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
    "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
    "\n",
    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
    "    model_name = \"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit\",\n",
    "    max_seq_length = max_seq_length,\n",
    "    dtype = dtype,\n",
    "    load_in_4bit = load_in_4bit,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6bZsfBuZDeCL",
    "outputId": "f857d68e-018a-473b-cb59-64790ca0205f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Unsloth 2024.7 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
     ]
    }
   ],
   "source": [
    "model = FastLanguageModel.get_peft_model(\n",
    "    model,\n",
    "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
    "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
    "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
    "    lora_alpha = 16,\n",
    "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
    "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
    "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
    "    use_gradient_checkpointing = False, # True or \"unsloth\" for very long context\n",
    "    random_state = 4249,\n",
    "    use_rslora = False,  # We support rank stabilized LoRA\n",
    "    loftq_config = None, # And LoftQ\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
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   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "template = \"\"\"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n",
    "\n",
    "{human}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n",
    "\n",
    "{gpt}<|eot_id|>\"\"\"\n",
    "\n",
    "def formatting_prompts_func(examples):\n",
    "    convos = examples[\"conversations\"]\n",
    "    texts = [template.format(human=convo[0][\"value\"], gpt=convo[1][\"value\"]) for convo in convos]\n",
    "    return { \"text\" : texts, }\n",
    "pass\n",
    "\n",
    "dataset = load_dataset('gsarti/eureka-rebus', 'llm_sft', data_files=[\"train.jsonl\"], split = \"train\")\n",
    "dataset = dataset.map(formatting_prompts_func, batched = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n",
      "\n",
      "Risolvi gli indizi tra parentesi per ottenere una prima lettura, e usa la chiave di lettura per ottenere la soluzione del rebus.\n",
      "\n",
      "Rebus: U [Lo è il passacavallo] LO [È fatta di vimini] F F [Sono decimi di chilo] S [Disusato soprabito] A [Un rampicante dei Tropici]\n",
      "Chiave di lettura: 3 6 12 8<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n",
      "\n",
      "Procediamo alla risoluzione del rebus passo per passo:\n",
      "- U = U\n",
      "- [Lo è il passacavallo] = nave\n",
      "- L O = L O\n",
      "- [È fatta di vimini] = cesta\n",
      "- F F = F F\n",
      "- [Sono decimi di chilo] = etti\n",
      "- S = S\n",
      "- [Disusato soprabito] = tait\n",
      "- A = A\n",
      "- [Un rampicante dei Tropici] = liana\n",
      "\n",
      "Prima lettura: U nave LO cesta F F etti S tait A liana\n",
      "\n",
      "Ora componiamo la soluzione seguendo la chiave risolutiva:\n",
      "3 = Una\n",
      "6 = veloce\n",
      "12 = staffettista\n",
      "8 = italiana\n",
      "\n",
      "Soluzione: Una veloce staffettista italiana\n",
      "<|eot_id|>\n"
     ]
    }
   ],
   "source": [
    "print(dataset[5][\"text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_dataset = load_dataset('gsarti/eureka-rebus', 'llm_sft', data_files=[\"id_test.jsonl\", \"ood_test.jsonl\"], split = \"train\")\n",
    "eval_dataset = eval_dataset.map(formatting_prompts_func, batched = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n",
      "\n",
      "Risolvi gli indizi tra parentesi per ottenere una prima lettura, e usa la chiave di lettura per ottenere la soluzione del rebus.\n",
      "\n",
      "Rebus: [Può avere il campanile] VIO [Possedevano la bacchetta magica] SO R [Componimento lirico] LL [Si innamorò di Narciso] [Il valore del suffisso\"esa\"] M [Gomma per suole] TE\n",
      "Chiave di lettura: 3 1 5 2 6 5 4 8<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n",
      "\n",
      "Procediamo alla risoluzione del rebus passo per passo:\n",
      "- [Può avere il campanile] = chiesa\n",
      "- V I O = V I O\n",
      "- [Possedevano la bacchetta magica] = fate\n",
      "- S O R = S O R\n",
      "- [Componimento lirico] = ode\n",
      "- L L = L L\n",
      "- [Si innamorò di Narciso] = eco\n",
      "- [Il valore del suffisso\"esa\"] = sei\n",
      "- M = M\n",
      "- [Gomma per suole] = para\n",
      "- T E = T E\n",
      "\n",
      "Prima lettura: chiesa VIO fate SO R ode LL eco sei M para TE\n",
      "\n",
      "Ora componiamo la soluzione seguendo la chiave risolutiva:\n",
      "3 = Chi\n",
      "1 = è\n",
      "5 = savio\n",
      "2 = fa\n",
      "6 = tesoro\n",
      "5 = delle\n",
      "4 = cose\n",
      "8 = imparate\n",
      "\n",
      "Soluzione: Chi è savio fa tesoro delle cose imparate\n",
      "<|eot_id|>\n"
     ]
    }
   ],
   "source": [
    "print(eval_dataset[5][\"text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67,
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    },
    "id": "95_Nn-89DhsL",
    "outputId": "1c2d9378-ce53-42b1-99e9-31a0b7aa530c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    }
   ],
   "source": [
    "from trl import SFTTrainer, DataCollatorForCompletionOnlyLM\n",
    "from transformers import TrainingArguments\n",
    "from unsloth import is_bfloat16_supported\n",
    "\n",
    "response_template = \"<|start_header_id|>assistant<|end_header_id|>\"\n",
    "collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)\n",
    "\n",
    "trainer = SFTTrainer(\n",
    "    model = model,\n",
    "    tokenizer = tokenizer,\n",
    "    train_dataset = dataset,\n",
    "    eval_dataset = eval_dataset,\n",
    "    dataset_text_field = \"text\",\n",
    "    max_seq_length = max_seq_length,\n",
    "    dataset_num_proc = 2,\n",
    "    packing = False,\n",
    "    data_collator=collator,\n",
    "    args = TrainingArguments(\n",
    "        per_device_train_batch_size = 4,\n",
    "        gradient_accumulation_steps = 8,\n",
    "        warmup_steps = 100,\n",
    "        num_train_epochs = 2,\n",
    "        learning_rate = 2e-4,\n",
    "        fp16 = not is_bfloat16_supported(),\n",
    "        bf16 = is_bfloat16_supported(),\n",
    "        logging_steps = 100,\n",
    "        optim = \"adamw_8bit\",\n",
    "        weight_decay = 0.01,\n",
    "        output_dir = \"outputs\",\n",
    "        lr_scheduler_type = \"linear\",\n",
    "        seed = 42,\n",
    "        save_steps = 500,\n",
    "        save_total_limit = 10,\n",
    "        eval_strategy = \"steps\",\n",
    "        eval_steps = 500,\n",
    "        push_to_hub = True,\n",
    "        hub_model_id = \"llama-3.1-8b-rebus-solver-adapters\",\n",
    "        report_to=\"wandb\"\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2ejIt2xSNKKp",
    "outputId": "255e97bb-635f-46bb-c4a7-5268f91d0aec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU = NVIDIA GeForce RTX 3090. Max memory = 23.691 GB.\n",
      "5.984 GB of memory reserved.\n"
     ]
    }
   ],
   "source": [
    "#@title Show current memory stats\n",
    "gpu_stats = torch.cuda.get_device_properties(0)\n",
    "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
    "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
    "print(f\"{start_gpu_memory} GB of memory reserved.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "id": "yqxqAZ7KJ4oL",
    "outputId": "285c6b1b-671f-4a13-9036-620a86fd7873"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
      "   \\\\   /|    Num examples = 81,136 | Num Epochs = 2\n",
      "O^O/ \\_/ \\    Batch size per device = 4 | Gradient Accumulation steps = 8\n",
      "\\        /    Total batch size = 32 | Total steps = 5,070\n",
      " \"-____-\"     Number of trainable parameters = 41,943,040\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mgsarti\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.17.5"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
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      "text/html": [
       "Run data is saved locally in <code>/wandb/run-20240724_122811-eofsunlg</code>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/gsarti/huggingface/runs/eofsunlg' target=\"_blank\">outputs</a></strong> to <a href='https://wandb.ai/gsarti/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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      ]
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     "metadata": {},
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     "data": {
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       " View project at <a href='https://wandb.ai/gsarti/huggingface' target=\"_blank\">https://wandb.ai/gsarti/huggingface</a>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
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       " View run at <a href='https://wandb.ai/gsarti/huggingface/runs/eofsunlg' target=\"_blank\">https://wandb.ai/gsarti/huggingface/runs/eofsunlg</a>"
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='5070' max='5070' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [5070/5070 8:19:04, Epoch 1/2]\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",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.038500</td>\n",
       "      <td>0.052560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.023800</td>\n",
       "      <td>0.043179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1500</td>\n",
       "      <td>0.017800</td>\n",
       "      <td>0.037215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>0.015100</td>\n",
       "      <td>0.036094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2500</td>\n",
       "      <td>0.011900</td>\n",
       "      <td>0.032255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3000</td>\n",
       "      <td>0.005900</td>\n",
       "      <td>0.032636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3500</td>\n",
       "      <td>0.006100</td>\n",
       "      <td>0.030985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4000</td>\n",
       "      <td>0.005100</td>\n",
       "      <td>0.031440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4500</td>\n",
       "      <td>0.004900</td>\n",
       "      <td>0.030930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5000</td>\n",
       "      <td>0.004400</td>\n",
       "      <td>0.030401</td>\n",
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    },
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    "outputId": "bb52857e-ae98-46b0-da3a-6416ff4af9e0"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29958.9032 seconds used for training.\n",
      "499.32 minutes used for training.\n",
      "Peak reserved memory = 17.939 GB.\n",
      "Peak reserved memory for training = 11.955 GB.\n",
      "Peak reserved memory % of max memory = 75.721 %.\n",
      "Peak reserved memory for training % of max memory = 50.462 %.\n"
     ]
    }
   ],
   "source": [
    "#@title Show final memory and time stats\n",
    "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
    "used_percentage = round(used_memory         /max_memory*100, 3)\n",
    "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n",
    "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
    "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n",
    "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
    "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
    "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
    "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save merged model in FP16\n",
    "model.push_to_hub_merged(\"llama-3.1-8b-rebus-solver-fp16\", tokenizer, save_method = \"merged_16bit\")\n",
    "\n",
    "# Save model to GGUF for local inference\n",
    "model.push_to_hub_gguf(\"llama-3.1-8b-rebus-solver-Q8_0-GGUF\", tokenizer)"
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