GENAI_NOTEBOOKS/Llama-3_Finetuning_on_custom_dataset_with_unsloth.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Llama-3 Finetuning on custom dataset with unsloth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2eSvM9zX_2d3"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "import torch\n",
    "\n",
    "major_version, minor_version = torch.cuda.get_device_capability()\n",
    "\n",
    "!pip install \"unsloth[colab-new] @git+https://github.com/unslothai/unsloth.git\"\n",
    "\n",
    "if major_version >= 8:\n",
    "    !pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes\n",
    "else:\n",
    "    !pip install --no-deps xformers trl peft accelerate bitsandbytes\n",
    "pass"
   ]
  },
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    "id": "QmUBVEnvCDJv",
    "outputId": "e14a20ca-5800-4653-9a6f-8a58513f642d"
   },
   "outputs": [],
   "source": [
    "from unsloth import FastLanguageModel\n",
    "import torch\n",
    "\n",
    "max_seq_length = 2048\n",
    "dtype = None\n",
    "load_in_4bit = True\n",
    "\n",
    "fourbit_models = [\n",
    "    \"unsloth/mistral-7b-bnb-4bit\",\n",
    "    \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n",
    "    \"unsloth/llama-2-7b-bnb-4bit\",\n",
    "    \"unsloth/gemma-7b-bnb-4bit\",\n",
    "    \"unsloth/gemma-7b-it-bnb-4bit\",\n",
    "    \"unsloth/gemma-2b-bnb-4bit\",\n",
    "    \"unsloth/gemma-2b-it-bnb-4bit\",\n",
    "    \"unsloth/llama-3-8b-bnb-4bit\",\n",
    "]\n",
    "\n",
    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
    "    model_name = \"unsloth/llama-3-8b-bnb-4bit\",\n",
    "    max_seq_length = max_seq_length,\n",
    "    dtype = dtype,\n",
    "    load_in_4bit = load_in_4bit,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SXd9bTZd1aaL"
   },
   "source": [
    "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6bZsfBuZDeCL",
    "outputId": "cda08fb6-f959-48c3-ead8-65a9d12c1596"
   },
   "outputs": [],
   "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 = \"unsloth\", # True or \"unsloth\" for very long context\n",
    "    random_state = 3407,\n",
    "    use_rslora = False,  # We support rank stabilized LoRA\n",
    "    loftq_config = None, # And LoftQ\n",
    ")"
   ]
  },
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    "id": "LjY75GoYUCB8",
    "outputId": "51d2433f-c01a-42f8-eb04-498d5406ae70"
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   "outputs": [],
   "source": [
    "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
    "\n",
    "### Instruction:\n",
    "{}\n",
    "\n",
    "### Input:\n",
    "{}\n",
    "\n",
    "### Response:\n",
    "{}\"\"\"\n",
    "\n",
    "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
    "\n",
    "def formatting_prompts_func(examples):\n",
    "    instructions = examples[\"instruction\"]\n",
    "    inputs       = examples[\"input\"]\n",
    "    outputs      = examples[\"output\"]\n",
    "    texts = []\n",
    "    for instruction, input, output in zip(instructions, inputs, outputs):\n",
    "        # Must add EOS_TOKEN, otherwise your generation will go on forever!\n",
    "        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n",
    "        texts.append(text)\n",
    "    return { \"text\" : texts, }\n",
    "pass\n",
    "\n",
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"yahma/alpaca-cleaned\", split = \"train\")\n",
    "\n",
    "dataset = dataset.map(formatting_prompts_func, batched = True,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "idAEIeSQ3xdS"
   },
   "source": [
    "<a name=\"Train\"></a>\n",
    "### Train the model\n",
    "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104,
     "referenced_widgets": [
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    },
    "id": "95_Nn-89DhsL",
    "outputId": "842ad9fb-3ce2-4668-a2bf-60f583d05a2f"
   },
   "outputs": [],
   "source": [
    "from trl import SFTTrainer\n",
    "from transformers import TrainingArguments\n",
    "\n",
    "trainer = SFTTrainer(\n",
    "    model = model,\n",
    "    tokenizer = tokenizer,\n",
    "    train_dataset = dataset,\n",
    "    dataset_text_field = \"text\",\n",
    "    max_seq_length = max_seq_length,\n",
    "    dataset_num_proc = 2,\n",
    "    packing = False, # Can make training 5x faster for short sequences.\n",
    "\n",
    "    args = TrainingArguments(\n",
    "        per_device_train_batch_size = 2,\n",
    "        gradient_accumulation_steps = 4,\n",
    "        warmup_steps = 5,\n",
    "        max_steps = 60,\n",
    "        learning_rate = 2e-4,\n",
    "        fp16 = not torch.cuda.is_bf16_supported(),\n",
    "        bf16 = torch.cuda.is_bf16_supported(),\n",
    "        logging_steps = 1,\n",
    "        optim = \"adamw_8bit\",\n",
    "        weight_decay = 0.01,\n",
    "        lr_scheduler_type = \"linear\",\n",
    "        seed = 3407,\n",
    "        output_dir = \"outputs\",\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU = NVIDIA GeForce RTX 3060. Max memory = 11.759 GB.\n",
      "0.0 GB of memory currently in use.\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    gpu_stats = torch.cuda.get_device_properties(0)\n",
    "    allocated_gpu_memory = round(torch.cuda.memory_allocated(0) / 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\"{allocated_gpu_memory} GB of memory currently in use.\")\n",
    "else:\n",
    "    print(\"CUDA is not available on this system.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "yqxqAZ7KJ4oL",
    "outputId": "bb0691f0-62f3-4576-d73a-d9daf92e89cf"
   },
   "outputs": [],
   "source": [
    "trainer_stats = trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "used_memory_for_lora = round(used_memory - allocated_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": "markdown",
   "metadata": {
    "id": "ekOmTR1hSNcr"
   },
   "source": [
    "<a name=\"Inference\"></a>\n",
    "### Inference\n",
    "Let's run the model! You can change the instruction and input - leave the output blank!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kR3gIAX-SM2q",
    "outputId": "cd29185d-4433-450f-ea19-f3ad6947ae3d"
   },
   "outputs": [],
   "source": [
    "FastLanguageModel.for_inference(model)\n",
    "\n",
    "inputs = tokenizer(\n",
    "[\n",
    "    alpaca_prompt.format(\n",
    "        \"Continue the fibonnaci sequence.\",\n",
    "        \"1, 1, 2, 3, 5, 8\",\n",
    "        \"\",\n",
    "    )\n",
    "], return_tensors = \"pt\").to(\"cuda\")\n",
    "\n",
    "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
    "\n",
    "tokenizer.batch_decode(outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CrSvZObor0lY"
   },
   "source": [
    " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e2pEuRb1r2Vg",
    "outputId": "0d250429-e431-4fc8-9edf-97e5fdc81da8"
   },
   "outputs": [],
   "source": [
    "FastLanguageModel.for_inference(model)\n",
    "inputs = tokenizer(\n",
    "[\n",
    "    alpaca_prompt.format(\n",
    "        \"Continue the fibonnaci sequence.\",\n",
    "        \"1, 1, 2, 3, 5, 8\",\n",
    "        \"\",\n",
    "    )\n",
    "], return_tensors = \"pt\").to(\"cuda\")\n",
    "\n",
    "from transformers import TextStreamer\n",
    "\n",
    "text_streamer = TextStreamer(tokenizer)\n",
    "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uMuVrWbjAzhc"
   },
   "source": [
    "<a name=\"Save\"></a>\n",
    "### Saving, loading finetuned models\n",
    "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
    "\n",
    "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "upcOlWe7A1vc"
   },
   "outputs": [],
   "source": [
    "model.save_pretrained(\"lora_model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AEEcJ4qfC7Lp"
   },
   "source": [
    "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MKX_XKs_BNZR",
    "outputId": "1e563fe1-7dd2-4851-99e0-017392b5cac6"
   },
   "outputs": [],
   "source": [
    "if False:\n",
    "    from unsloth import FastLanguageModel\n",
    "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
    "        model_name = \"lora_model\",\n",
    "        max_seq_length = max_seq_length,\n",
    "        dtype = dtype,\n",
    "        load_in_4bit = load_in_4bit,\n",
    "    )\n",
    "    FastLanguageModel.for_inference(model)\n",
    "inputs = tokenizer(\n",
    "[\n",
    "    alpaca_prompt.format(\n",
    "        \"What is a famous tall tower in Paris?\",\n",
    "        \"\",\n",
    "        \"\",\n",
    "    )\n",
    "], return_tensors = \"pt\").to(\"cuda\")\n",
    "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
    "tokenizer.batch_decode(outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QQMjaNrjsU5_"
   },
   "source": [
    "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yFfaXG0WsQuE"
   },
   "outputs": [],
   "source": [
    "if False:\n",
    "    from peft import AutoPeftModelForCausalLM\n",
    "    from transformers import AutoTokenizer\n",
    "    model = AutoPeftModelForCausalLM.from_pretrained(\n",
    "        \"lora_model\",\n",
    "        load_in_4bit = load_in_4bit,\n",
    "    )\n",
    "    tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f422JgM9sdVT"
   },
   "source": [
    "### Saving to float16 for VLLM\n",
    "\n",
    "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iHjt_SMYsd3P"
   },
   "outputs": [],
   "source": [
    "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
    "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n",
    "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
    "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n",
    "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n",
    "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TCv4vXHd61i7"
   },
   "source": [
    "### GGUF / llama.cpp Conversion\n",
    "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
    "\n",
    "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
    "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
    "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
    "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "FqfebeAdT073"
   },
   "outputs": [],
   "source": [
    "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
    "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
    "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
    "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n",
    "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
    "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bDp0zNpwe6U_"
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   "source": [
    "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html)."
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  }
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