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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+alpaca_data_cleaned.json filter=lfs diff=lfs merge=lfs -text

Alpaca_+_Llama3_8b_finetuning_with_own_data.ipynb

@@ -0,0 +1,690 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\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> 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).\n",
+        "\n",
+        "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp).\n",
+        "\n",
+        "**[NEW] Llama-3 8b is trained on a crazy 15 trillion tokens! Llama-2 was 2 trillion.**\n",
+        "\n",
+        "Use our [Llama-3 8b Instruct](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing) notebook for conversational style finetunes.\n",
+        "\n",
+        "https://github.com/unslothai/unsloth?tab=readme-ov-file"
+      ],
+      "metadata": {
+        "id": "IqM-T1RTzY6C"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "2eSvM9zX_2d3"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "* We support Llama, Mistral, Phi-3, Gemma, Yi, DeepSeek, Qwen, TinyLlama, Vicuna, Open Hermes etc\n",
+        "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n",
+        "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n",
+        "* With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.\n",
+        "* [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)"
+      ],
+      "metadata": {
+        "id": "r2v_X2fA0Df5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 356
+        },
+        "id": "QmUBVEnvCDJv",
+        "outputId": "e6e9635b-059f-4865-9650-5adb5ba76626"
+      },
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "RuntimeError",
+          "evalue": "Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-6-b727a954f97e>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0munsloth\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFastLanguageModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmax_seq_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2048\u001b[0m \u001b[0;31m# Choose any! We auto support RoPE Scaling internally!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;31m# None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mload_in_4bit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m \u001b[0;31m# Use 4bit quantization to reduce memory usage. Can be False.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;31m# Fix up is_bf16_supported https://github.com/unslothai/unsloth/issues/504\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m \u001b[0mmajor_version\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminor_version\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_device_capability\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m \u001b[0mSUPPORTS_BFLOAT16\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmajor_version\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mis_bf16_supported\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mSUPPORTS_BFLOAT16\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36mget_device_capability\u001b[0;34m(device)\u001b[0m\n\u001b[1;32m    428\u001b[0m         \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmajor\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mminor\u001b[0m \u001b[0mcuda\u001b[0m \u001b[0mcapability\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m     \"\"\"\n\u001b[0;32m--> 430\u001b[0;31m     \u001b[0mprop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_device_properties\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36mget_device_properties\u001b[0;34m(device)\u001b[0m\n\u001b[1;32m    442\u001b[0m         \u001b[0m_CudaDeviceProperties\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mproperties\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m     \"\"\"\n\u001b[0;32m--> 444\u001b[0;31m     \u001b[0m_lazy_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# will define _get_device_properties\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    445\u001b[0m     \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_device_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptional\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdevice\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mdevice\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mdevice_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m    291\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m\"CUDA_MODULE_LOADING\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m             \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"CUDA_MODULE_LOADING\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"LAZY\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    294\u001b[0m         \u001b[0;31m# Some of the queued calls may reentrantly call _lazy_init();\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m         \u001b[0;31m# we need to just return without initializing in that case.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mRuntimeError\u001b[0m: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx"
+          ]
+        }
+      ],
+      "source": [
+        "from unsloth import FastLanguageModel\n",
+        "import torch\n",
+        "max_seq_length = 2048 # 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",
+        "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
+        "fourbit_models = [\n",
+        "    \"unsloth/mistral-7b-v0.3-bnb-4bit\",      # New Mistral v3 2x faster!\n",
+        "    \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n",
+        "    \"unsloth/llama-3-8b-bnb-4bit\",           # Llama-3 15 trillion tokens model 2x faster!\n",
+        "    \"unsloth/llama-3-8b-Instruct-bnb-4bit\",\n",
+        "    \"unsloth/llama-3-70b-bnb-4bit\",\n",
+        "    \"unsloth/Phi-3-mini-4k-instruct\",        # Phi-3 2x faster!\n",
+        "    \"unsloth/Phi-3-medium-4k-instruct\",\n",
+        "    \"unsloth/mistral-7b-bnb-4bit\",\n",
+        "    \"unsloth/gemma-7b-bnb-4bit\",             # Gemma 2.2x faster!\n",
+        "] # More models at https://huggingface.co/unsloth\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",
+      "source": [
+        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
+      ],
+      "metadata": {
+        "id": "SXd9bTZd1aaL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6bZsfBuZDeCL"
+      },
+      "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",
+      "source": [
+        "<a name=\"Data\"></a>\n",
+        "### Data Prep\n",
+        "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n",
+        "\n",
+        "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n",
+        "\n",
+        "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n",
+        "\n",
+        "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n",
+        "\n",
+        "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)."
+      ],
+      "metadata": {
+        "id": "vITh0KVJ10qX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LjY75GoYUCB8"
+      },
+      "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",
+        "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",
+        "dataset = load_dataset(\"harry85ma/alpaca-cleaned\", split = \"train\")\n",
+        "dataset = dataset.map(formatting_prompts_func, batched = True,)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#dataset.head()"
+      ],
+      "metadata": {
+        "id": "GJJqORnVbJ_4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "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`!"
+      ],
+      "metadata": {
+        "id": "idAEIeSQ3xdS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "95_Nn-89DhsL"
+      },
+      "outputs": [],
+      "source": [
+        "from trl import SFTTrainer\n",
+        "from transformers import TrainingArguments\n",
+        "from unsloth import is_bfloat16_supported\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",
+        "    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 is_bfloat16_supported(),\n",
+        "        bf16 = is_bfloat16_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": null,
+      "metadata": {
+        "id": "2ejIt2xSNKKp",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0250e965-0a17-411b-a8b8-699525468de3",
+        "cellView": "form"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "GPU = NVIDIA L4. Max memory = 22.168 GB.\n",
+            "5.594 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": null,
+      "metadata": {
+        "id": "yqxqAZ7KJ4oL"
+      },
+      "outputs": [],
+      "source": [
+        "trainer_stats = trainer.train()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pCqnaKmlO1U9"
+      },
+      "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",
+      "source": [
+        "<a name=\"Inference\"></a>\n",
+        "### Inference\n",
+        "Let's run the model! You can change the instruction and input - leave the output blank!"
+      ],
+      "metadata": {
+        "id": "ekOmTR1hSNcr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# alpaca_prompt = Copied from above\n",
+        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
+        "inputs = tokenizer(\n",
+        "[\n",
+        "    alpaca_prompt.format(\n",
+        "        \"Given a positive integer, generate a sequence of numbers leading to 1.\", # instruction\n",
+        "        \"Number: 6\", # 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)"
+      ],
+      "metadata": {
+        "id": "kR3gIAX-SM2q",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 211
+        },
+        "outputId": "0457bacb-c2c2-4305-b6e9-b6508dadb406"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'FastLanguageModel' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-3-1f439cfcb545>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# alpaca_prompt = Copied from above\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mFastLanguageModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_inference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Enable native 2x faster inference\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m inputs = tokenizer(\n\u001b[1;32m      4\u001b[0m [\n\u001b[1;32m      5\u001b[0m     alpaca_prompt.format(\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'FastLanguageModel' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "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!"
+      ],
+      "metadata": {
+        "id": "CrSvZObor0lY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# alpaca_prompt = Copied from above\n",
+        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\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",
+        "text_streamer = TextStreamer(tokenizer)\n",
+        "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
+      ],
+      "metadata": {
+        "id": "e2pEuRb1r2Vg",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7847523b-2a25-46d7-c796-317c2e8a359c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<|begin_of_text|>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",
+            "Continue the fibonnaci sequence.\n",
+            "\n",
+            "### Input:\n",
+            "1, 1, 2, 3, 5, 8\n",
+            "\n",
+            "### Response:\n",
+            "11, 18, 29, 47, 76, 123<|end_of_text|>\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "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!"
+      ],
+      "metadata": {
+        "id": "uMuVrWbjAzhc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.save_pretrained(\"lora_model\") # Local saving\n",
+        "tokenizer.save_pretrained(\"lora_model\")\n",
+        "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
+        "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
+      ],
+      "metadata": {
+        "id": "upcOlWe7A1vc",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5e875fd0-6e80-4917-8776-316409fd9c5f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "('lora_model/tokenizer_config.json',\n",
+              " 'lora_model/special_tokens_map.json',\n",
+              " 'lora_model/tokenizer.json')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
+      ],
+      "metadata": {
+        "id": "AEEcJ4qfC7Lp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "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)"
+      ],
+      "metadata": {
+        "id": "MKX_XKs_BNZR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "be14674f-5865-40b8-8d9d-b93327464f13"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['<|begin_of_text|>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:\\nWhat is a famous tall tower in Paris?\\n\\n### Input:\\n\\n\\n### Response:\\nOne of the most famous tall towers in Paris is the Eiffel Tower. Built in 1889, it stands at 324 meters tall and is located on the Champ de Mars in the 7th arrondissement of Paris. The Eiffel Tower is a wrought-iron lattice tower designed by the French']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "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**."
+      ],
+      "metadata": {
+        "id": "QQMjaNrjsU5_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if False:\n",
+        "    # I highly do NOT suggest - use Unsloth if possible\n",
+        "    from peft import AutoPeftModelForCausalLM\n",
+        "    from transformers import AutoTokenizer\n",
+        "    model = AutoPeftModelForCausalLM.from_pretrained(\n",
+        "        \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
+        "        load_in_4bit = load_in_4bit,\n",
+        "    )\n",
+        "    tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
+      ],
+      "metadata": {
+        "id": "yFfaXG0WsQuE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "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."
+      ],
+      "metadata": {
+        "id": "f422JgM9sdVT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "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 = \"\")"
+      ],
+      "metadata": {
+        "id": "iHjt_SMYsd3P"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "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."
+      ],
+      "metadata": {
+        "id": "TCv4vXHd61i7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "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 = \"\")"
+      ],
+      "metadata": {
+        "id": "FqfebeAdT073"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "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)."
+      ],
+      "metadata": {
+        "id": "bDp0zNpwe6U_"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
+        "\n",
+        "Some other links:\n",
+        "1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n",
+        "2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n",
+        "3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n",
+        "4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n",
+        "5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n",
+        "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n",
+        "7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n",
+        "8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n",
+        "9. [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)\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.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> Support our work if you can! Thanks!\n",
+        "</div>"
+      ],
+      "metadata": {
+        "id": "Zt9CHJqO6p30"
+      }
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

README.md

@@ -1,3 +1,202 @@
----
-license: mit
----
+---
+base_model: unsloth/llama-3-8b-bnb-4bit
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.11.1

adapter_config.json

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alpaca_data_cleaned.json

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alpaca_data_cleaned_harry.json

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+        "output": "Haris Hota is a lead data scientist from frankfurt and likes datascience , ML, DL and GenAI"
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+        "output": "Haris Hota lives in Frankfurt Germany"
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+
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