Llama_3_Finetuning_ORPO_with_Unsloth.ipynb

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  "cells": [
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      "cell_type": "markdown",
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      "source": [
        "## Finetune Llama-3-8B with unsloth | 4bit quantized with ORPO\n",
        "\n",
        "Odds Ratio Preference Optimization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8lSNa9FYvmA7"
      },
      "outputs": [],
      "source": [
        "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
        "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
        "\n",
        "!pip install --no-deps \"xformers<0.0.26\" trl peft accelerate bitsandbytes"
      ]
    },
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        "id": "2eSvM9zX_2d3",
        "outputId": "d0b6c66e-ab67-4241-bdf1-b1babc18c2d6"
      },
      "outputs": [],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "\n",
        "max_seq_length = 4096 # 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",
        "\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",
        "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_H6EWbe-t_bk"
      },
      "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": "34834b73-2386-45f5-d197-7f610106a053"
      },
      "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",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNfc6OLOuuyZ"
      },
      "source": [
        "<a name=\"Data\"></a>\n",
        "### Data Prep\n",
        "We now use a special ORPO style dataset from [recipe-research](https://huggingface.co/datasets/reciperesearch/dolphin-sft-v0.1-preference).\n",
        "\n",
        "You need at least 3 columns:\n",
        "* Instruction\n",
        "* Accepted\n",
        "* Rejected\n",
        "\n",
        "For example:\n",
        "* Instruction: \"What is 2+2?\"\n",
        "* Accepted: \"The answer is 4\"\n",
        "* Rejected: \"The answer is 5\"\n",
        "\n",
        "The goal of ORPO is to penalize the \"rejected\" samples, and increase the likelihood of \"accepted\" samples. [recipe-research](https://huggingface.co/datasets/reciperesearch/dolphin-sft-v0.1-preference) essentially used Mistral to generate the \"rejected\" responses, and used GPT-4 to generated the \"accepted\" responses."
      ]
    },
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      "source": [
        "# The data must be formatted with appropriate prompt template first.\n",
        "# See details here: https://github.com/huggingface/trl/blob/main/examples/scripts/orpo.py\n",
        "\n",
        "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 format_prompt(sample):\n",
        "    instruction = sample[\"instruction\"]  # Extract the instruction from the sample\n",
        "    input = sample[\"input\"]              # Extract the input from the sample\n",
        "    accepted = sample[\"accepted\"]        # Extract the accepted response from the sample\n",
        "    rejected = sample[\"rejected\"]        # Extract the rejected response from the sample\n",
        "\n",
        "    # ORPOTrainer expects prompt/chosen/rejected keys\n",
        "    sample[\"prompt\"] = alpaca_prompt.format(instruction, input, \"\")  # Format the prompt using the template\n",
        "    sample[\"chosen\"] = accepted + EOS_TOKEN  # Append EOS token to the accepted response\n",
        "    sample[\"rejected\"] = rejected + EOS_TOKEN  # Append EOS token to the rejected response\n",
        "\n",
        "    return sample  # Return the modified sample\n",
        "\n",
        "pass\n",
        "\n",
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"reciperesearch/dolphin-sft-v0.1-preference\")[\"train\"]\n",
        "\n",
        "# This function is then applied to each sample in the dataset using dataset.map,\n",
        "# which processes each sample through the format_prompt function and returns the formatted dataset.\n",
        "dataset = dataset.map(format_prompt,)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oqyleKojqgDq"
      },
      "outputs": [],
      "source": [
        "# Enable reward modelling stats\n",
        "from unsloth import PatchDPOTrainer\n",
        "PatchDPOTrainer()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1ZlnJpkxIuV"
      },
      "source": [
        "<a name=\"Train\"></a>\n",
        "### Train the model\n",
        "Now let's use Huggingface TRL's `ORPOTrainer`! More docs here: [TRL ORPO docs](https://huggingface.co/docs/trl/main/en/orpo_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": 122,
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      "outputs": [],
      "source": [
        "from trl import ORPOConfig, ORPOTrainer\n",
        "\n",
        "orpo_trainer = ORPOTrainer(\n",
        "    model = model,\n",
        "    train_dataset = dataset,\n",
        "    tokenizer = tokenizer,\n",
        "    args = ORPOConfig(\n",
        "        max_length = max_seq_length,\n",
        "        max_prompt_length = max_seq_length//2,\n",
        "        max_completion_length = max_seq_length//2,\n",
        "        per_device_train_batch_size = 2,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        beta = 0.1,\n",
        "        logging_steps = 1,\n",
        "        optim = \"adamw_8bit\",\n",
        "        lr_scheduler_type = \"linear\",\n",
        "        max_steps = 30, # Change to num_train_epochs = 1 for full training runs\n",
        "        fp16 = not torch.cuda.is_bf16_supported(),\n",
        "        bf16 = torch.cuda.is_bf16_supported(),\n",
        "        output_dir = \"outputs\",\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EWGFqAo5Q2me",
        "outputId": "34e5a57e-fe2a-4b44-99fc-cdbccf669d70"
      },
      "outputs": [],
      "source": [
        "orpo_trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FgEvCW76xblp"
      },
      "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": "0DJPbbtGxcFJ",
        "outputId": "6b25cdda-c86f-4f25-99e7-90c170d97197"
      },
      "outputs": [],
      "source": [
        "# alpaca_prompt = Copied from above\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    alpaca_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",
        "], 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": {},
      "source": [
        "Got output of 13 (i.e. 5 + 8)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "absTV8M1xzwz"
      },
      "source": [
        " ### You can also use a `TextStreamer`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xKzWRN1Px0Bg",
        "outputId": "fcbe0342-a227-4a1d-be7b-039f37193ec3"
      },
      "outputs": [],
      "source": [
        "# alpaca_prompt = Copied from above\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    alpaca_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",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "\n",
        "text_streamer = TextStreamer(tokenizer)\n",
        "\n",
        "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### explanation of the `FastLanguageModel.for_inference` \n",
        "\n",
        "📌 The `for_inference` method is a static method of the `FastLanguageModel` class that prepares a trained model for inference by modifying its configuration and wrapping the `generate` method for faster inference.\n",
        "\n",
        "📌 It first recursively sets the `gradient_checkpointing` attribute of the model and all its submodules to `False`, and the `training` attribute to `False`, to disable gradient checkpointing and training mode during inference."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y_3rdZXmx3Hh"
      },
      "source": [
        "## Saving, loading finetuned models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJbRqLynx3a8",
        "outputId": "05228b0d-4490-49f1-cd71-5cfe0f9417c9"
      },
      "outputs": [],
      "source": [
        "model.save_pretrained(\"lora_model\") # Local saving\n",
        "\n",
        "tokenizer.save_pretrained(\"lora_model\")\n",
        "\n",
        "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
        "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mwIRb8DByBGg"
      },
      "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": "UPHJs9wDyBbN",
        "outputId": "77165645-7402-4347-a613-f0ad90b572d9"
      },
      "outputs": [],
      "source": [
        "if False:\n",
        "    from unsloth import FastLanguageModel\n",
        "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "        model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
        "        max_seq_length = max_seq_length,\n",
        "        dtype = dtype,\n",
        "        load_in_4bit = load_in_4bit,\n",
        "    )\n",
        "    FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "\n",
        "# alpaca_prompt = You MUST copy from above!\n",
        "\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    alpaca_prompt.format(\n",
        "        \"What is a famous tall tower in Paris?\", # instruction\n",
        "        \"\", # input\n",
        "        \"\", # output - leave this blank for generation!\n",
        "    )\n",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
        "tokenizer.batch_decode(outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPSrEDuoyE5_"
      },
      "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": "sTO5VnItyC08"
      },
      "outputs": [],
      "source": [
        "if False:\n",
        "    # I highly do NOT suggest - use Unsloth if possible\n",
        "    from peft import AutoPeftModelForCausalLM\n",
        "    from transformers import AutoTokenizer\n",
        "\n",
        "    model = AutoPeftModelForCausalLM.from_pretrained(\n",
        "        \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
        "        load_in_4bit = load_in_4bit,\n",
        "    )\n",
        "\n",
        "    tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l10uNsFYyGav"
      },
      "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": "fZR6n7DsyHu6"
      },
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      "source": [
        "# Merge to 16bit\n",
        "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",
        "\n",
        "# Merge to 4bit\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",
        "\n",
        "# Just LoRA adapters\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": "ssjW3ST2yI1L"
      },
      "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": null,
      "metadata": {
        "id": "FKVTNAwyyKFR"
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      "source": [
        "# Save to 8bit Q8_0\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
        "\n",
        "# Save to 16bit GGUF\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",
        "\n",
        "# Save to q4_k_m GGUF\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": "TL-PCYb1yMzx"
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        "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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