TinyLlama_with_Unsloth_and_RoPE_Scaling_dolly-15k.ipynb

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      "cell_type": "markdown",
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      "source": [
        "## Finetune tiny-llama in Google Colab with unsloth - dolly-15k dataset\n",
        "\n",
        "Below notebook mostly replicates an example given in the [official unsloth example ](https://github.com/unslothai/unsloth)\n",
        "\n",
        "I have only made few minor changes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import torch\n",
        "from datasets import load_dataset\n",
        "from unsloth import FastLanguageModel"
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
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        "id": "QmUBVEnvCDJv",
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      "source": [
        "max_seq_length = 4096 # Choose any as, unsloth supports 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/tinyllama-bnb-4bit\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Supported models - \n",
        "\n",
        "https://github.com/unslothai/unsloth?tab=readme-ov-file#documentation\n",
        "\n",
        "```py\n",
        "# 4bit pre quantized models we support - 4x faster downloading!\n",
        "fourbit_models = [\n",
        "    \"unsloth/mistral-7b-bnb-4bit\",\n",
        "    \"unsloth/llama-2-7b-bnb-4bit\",\n",
        "    \"unsloth/llama-2-13b-bnb-4bit\",\n",
        "    \"unsloth/codellama-34b-bnb-4bit\",\n",
        "    \"unsloth/tinyllama-bnb-4bit\",\n",
        "]\n",
        "\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S3xvsMEWyJbZ"
      },
      "source": [
        "TinyLlama's internal maximum sequence length is 2048. \n",
        "\n",
        "However, RoPE Scaling is used here to extend it to 4096 with Unsloth\n",
        "\n",
        "Unsloth implements RoPE with Triton to accelerate models further.\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!\n",
        "\n",
        "**[NOTE]** We set `gradient_checkpointing=False` ONLY for TinyLlama since Unsloth saves tonnes of memory usage. This does NOT work for `llama-2-7b` or `mistral-7b` since the memory usage will still exceed Tesla T4's 15GB. GC recomputes the forward pass during the backward pass, saving loads of memory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bZsfBuZDeCL",
        "outputId": "3de492b6-0bb6-4fd1-a0e5-f5e0ef36dcd5"
      },
      "outputs": [],
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 32, # 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 = 32,\n",
        "    lora_dropout = 0, # Currently only supports dropout = 0\n",
        "    bias = \"none\",    # Currently only supports bias = \"none\"\n",
        "    use_gradient_checkpointing = False, # With Unsloth, we can turn this off!\n",
        "    random_state = 3407,\n",
        "    max_seq_length = max_seq_length,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vITh0KVJ10qX"
      },
      "source": [
        "Here I use databricks-dolly-15k which is an open source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization."
      ]
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        "id": "LjY75GoYUCB8",
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      "outputs": [],
      "source": [
        "dolly_15k_prompt = \"\"\"Below is an instruction that describes a task, paired with a context for that instruction. Write a response that appropriately completes the instruction.\n",
        "\n",
        "### Instruction:\n",
        "{}\n",
        "\n",
        "### Context:\n",
        "{}\n",
        "\n",
        "### Response:\n",
        "{}\"\"\"\n",
        "\n",
        "def formatting_prompts_func(examples):\n",
        "    instructions = examples[\"instruction\"]\n",
        "    context = examples.get(\"context\", [None] * len(instructions))\n",
        "    response = examples[\"response\"]\n",
        "    texts = []\n",
        "    for instruction, context_value, response_value in zip(instructions, context, response):\n",
        "        if context_value:  # Use the context if it exists\n",
        "            text = dolly_15k_prompt.format(instruction, context_value, response_value)\n",
        "        else:\n",
        "            text = \"\"  # Or use an empty string (or another placeholder) if context is missing\n",
        "        texts.append(text)\n",
        "    return {\"text\": texts}\n",
        "\n",
        "\n",
        "pass\n",
        "\n",
        "dataset = load_dataset(\"databricks/databricks-dolly-15k\", split = \"train\")\n",
        "\n",
        "dataset = dataset.map(formatting_prompts_func, batched = True,)\n",
        "\n",
        "# Filter out rows where 'text' is empty\n",
        "dataset = dataset.filter(lambda example: example['text'] != \"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idAEIeSQ3xdS"
      },
      "source": [
        "<a name=\"Train\"></a>\n",
        "\n",
        "### Train the model\n",
        "\n",
        "Now use TRL’s SFTTrainer to fine-tune models. \n",
        "\n",
        "[TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). \n",
        "\n",
        "We also support TRL's `DPOTrainer`! See our DPO tutorial on a free Google Colab instance [here](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing).\n",
        "\n",
        "------\n",
        "Official Doc of SFTTrainer - https://huggingface.co/docs/trl/main/en/sft_trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211,
          "referenced_widgets": [
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        },
        "id": "95_Nn-89DhsL",
        "outputId": "0be04edc-0aff-481c-a52f-fce8283ac502"
      },
      "outputs": [],
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments\n",
        "from transformers.utils import logging\n",
        "logging.set_verbosity_info()\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    train_dataset = dataset,\n",
        "    dataset_text_field = \"text\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    packing = True, # Packs short sequences together to save time!\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 2,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        warmup_ratio = 0.1,\n",
        "        num_train_epochs = 1,\n",
        "        learning_rate = 2e-5,\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 = 0.1,\n",
        "        lr_scheduler_type = \"linear\",\n",
        "        seed = 3407,\n",
        "        output_dir = \"outputs\",\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ejIt2xSNKKp",
        "outputId": "b58a5395-e0e3-43eb-b5dc-fd656d571a3d"
      },
      "outputs": [],
      "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": null,
      "metadata": {
        "id": "yqxqAZ7KJ4oL"
      },
      "outputs": [],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCqnaKmlO1U9",
        "outputId": "ecb21ea9-b0f5-48ab-9145-5baedb68a203"
      },
      "outputs": [],
      "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": "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": "2283bb0c-d568-4c92-8352-094ba12eaf06"
      },
      "outputs": [],
      "source": [
        "inputs = tokenizer(\n",
        "[\n",
        "    dolly_15k_prompt.format(\n",
        "        \"Continue the fibonnaci sequence.\", # instruction\n",
        "        \"1, 1, 2, 3, 5, 8\", # input\n",
        "        \"\", # output - leave this blank for generation!\n",
        "    )\n",
        "]*1, return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n",
        "tokenizer.batch_decode(outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMuVrWbjAzhc"
      },
      "source": [
        "<a name=\"Save\"></a>\n",
        "### Saving, loading finetuned models\n",
        "To save the final model, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "upcOlWe7A1vc"
      },
      "outputs": [],
      "source": [
        "model.save_pretrained(\"lora_model\") # Local saving\n",
        "# model.push_to_hub(\"your_name/lora_model\") # Online saving"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1bSoOaDunRPL"
      },
      "source": [
        "To save to `GGUF` / `llama.cpp`, or for model merging, use `model.merge_and_unload` first, then save the model. Maxime Labonne's [llm-course](https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html) has a nice tutorial on converting HF to GGUF! This [issue](https://github.com/ggerganov/llama.cpp/issues/3097) might be helpful for more info."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xcRjsZe0RK1b",
        "outputId": "541da627-2c0e-4e66-d99e-b1c8f53231d3"
      },
      "outputs": [],
      "source": [
        "model = model.merge_and_unload()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AEEcJ4qfC7Lp"
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      "source": [
        "Now if you want to load the LoRA adapters we just saved, we can!"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "id": "MKX_XKs_BNZR"
      },
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      "source": [
        "from peft import PeftModel\n",
        "model = PeftModel.from_pretrained(model, \"lora_model\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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        "Finally, we can now do some inference on the loaded model."
      ]
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        "inputs = tokenizer(\n",
        "[\n",
        "    dolly_15k_prompt.format(\n",
        "        \"What is the famous tower in France called?\", # instruction\n",
        "        \"\", # input\n",
        "        \"\", # output\n",
        "    )\n",
        "]*1, return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n",
        "tokenizer.batch_decode(outputs)"
      ]
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