diff --git a/Quantize_Llama_2_models_using_GGUF_and_llama_cpp.ipynb b/Quantize_Llama_2_models_using_GGUF_and_llama_cpp.ipynb index cb3fc28..a06c70a 100644 --- a/Quantize_Llama_2_models_using_GGUF_and_llama_cpp.ipynb +++ b/Quantize_Llama_2_models_using_GGUF_and_llama_cpp.ipynb @@ -1,1075 +1,67 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "gpuType": "T4", - "authorship_tag": "ABX9TyMohoDhmmKsuh9OLDHor3GB", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] }, - "language_info": { - "name": "python" + { + "cell_type": "markdown", + "metadata": { + "id": "8y_Rk94LzG7I" + }, + "source": [ + "# Quantize Llama 2 models using GGUF and llama.cpp\n", + "> 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course)\n", + "\n", + "❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne).\n", + "\n", + "## Usage\n", + "\n", + "* `MODEL_ID`: The ID of the model to quantize (e.g., `mlabonne/EvolCodeLlama-7b`).\n", + "* `QUANTIZATION_METHOD`: The quantization method to use.\n", + "\n", + "## Quantization methods\n", + "\n", + "The names of the quantization methods follow the naming convention: \"q\" + the number of bits + the variant used (detailed below). Here is a list of all the possible quant methods and their corresponding use cases, based on model cards made by [TheBloke](https://huggingface.co/TheBloke/):\n", + "\n", + "* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.\n", + "* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K\n", + "* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K\n", + "* `q3_k_s`: Uses Q3_K for all tensors\n", + "* `q4_0`: Original quant method, 4-bit.\n", + "* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.\n", + "* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K\n", + "* `q4_k_s`: Uses Q4_K for all tensors\n", + "* `q5_0`: Higher accuracy, higher resource usage and slower inference.\n", + "* `q5_1`: Even higher accuracy, resource usage and slower inference.\n", + "* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K\n", + "* `q5_k_s`: Uses Q5_K for all tensors\n", + "* `q6_k`: Uses Q8_K for all tensors\n", + "* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.\n", + "\n", + "As a rule of thumb, **I recommend using Q5_K_M** as it preserves most of the model's performance. Alternatively, you can use Q4_K_M if you want to save some memory. In general, K_M versions are better than K_S versions. I cannot recommend Q2_K or Q3_* versions, as they drastically decrease model performance." + ] }, - "accelerator": "GPU", - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "c281b60e104f4c5da547bbdd7208d4bc": { - "model_module": "@jupyter-widgets/controls", - "model_name": "VBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "VBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "VBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_2e2fabac70484c1c8b16fa6ca8fd8537", - "IPY_MODEL_bf53c635fa374420ad850eea22cd1e31", - "IPY_MODEL_065d59126a734c1aa096ba40cd4a129f", - "IPY_MODEL_e8855d5678a342f5a33171aa74d3b7bc" - ], - "layout": "IPY_MODEL_1c8a6b959f9c4443a92f58eff1b03077" - } - }, - "74b084c97f6f46d293a197bf9804460c": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_9fb5726f91734b1da149784680dc9624", - "placeholder": "​", - "style": "IPY_MODEL_202a8eb11eda4e58942113fbeacfdc3d", - "value": "

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"* `MODEL_ID`: The ID of the model to quantize (e.g., `mlabonne/EvolCodeLlama-7b`).\n", - "* `QUANTIZATION_METHOD`: The quantization method to use.\n", - "\n", - "## Quantization methods\n", - "\n", - "The names of the quantization methods follow the naming convention: \"q\" + the number of bits + the variant used (detailed below). Here is a list of all the possible quant methods and their corresponding use cases, based on model cards made by [TheBloke](https://huggingface.co/TheBloke/):\n", - "\n", - "* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.\n", - "* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K\n", - "* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K\n", - "* `q3_k_s`: Uses Q3_K for all tensors\n", - "* `q4_0`: Original quant method, 4-bit.\n", - "* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.\n", - "* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K\n", - "* `q4_k_s`: Uses Q4_K for all tensors\n", - "* `q5_0`: Higher accuracy, higher resource usage and slower inference.\n", - "* `q5_1`: Even higher accuracy, resource usage and slower inference.\n", - "* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K\n", - "* `q5_k_s`: Uses Q5_K for all tensors\n", - "* `q6_k`: Uses Q8_K for all tensors\n", - "* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.\n", - "\n", - "As a rule of thumb, **I recommend using Q5_K_M** as it preserves most of the model's performance. Alternatively, you can use Q4_K_M if you want to save some memory. In general, K_M versions are better than K_S versions. I cannot recommend Q2_K or Q3_* versions, as they drastically decrease model performance." - ], - "metadata": { - "id": "8y_Rk94LzG7I" - } - }, - { - "cell_type": "code", - "source": [ - "# Variables\n", - "MODEL_ID = \"mlabonne/EvolCodeLlama-7b\"\n", - "QUANTIZATION_METHODS = [\"q4_k_m\", \"q5_k_m\"]\n", - "\n", - "# Constants\n", - "MODEL_NAME = MODEL_ID.split('/')[-1]\n", - "\n", - "# Install llama.cpp\n", - "!git clone https://github.com/ggerganov/llama.cpp\n", - "!cd llama.cpp && git pull && make clean && LLAMA_CUBLAS=1 make\n", - "!pip install -r llama.cpp/requirements.txt\n", - "\n", - "# Download model\n", - "!git lfs install\n", - "!git clone https://huggingface.co/{MODEL_ID}\n", - "\n", - "# Convert to fp16\n", - "fp16 = f\"{MODEL_NAME}/{MODEL_NAME.lower()}.fp16.bin\"\n", - "!python llama.cpp/convert.py {MODEL_NAME} --outtype f16 --outfile {fp16}\n", - "\n", - "# Quantize the model for each method in the QUANTIZATION_METHODS list\n", - "for method in QUANTIZATION_METHODS:\n", - " qtype = f\"{MODEL_NAME}/{MODEL_NAME.lower()}.{method.upper()}.gguf\"\n", - " !./llama.cpp/quantize {fp16} {qtype} {method}" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, "id": "fD24jJxq7t3k", "outputId": "94954934-0829-44e9-a5e5-262c17e162d0" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "ggml_init_cublas: found 1 CUDA devices:\n", " Device 0: Tesla T4, compute capability 7.5\n", @@ -1684,36 +676,112 @@ "main: total time = 1089230.46 ms\n" ] } + ], + "source": [ + "# Variables\n", + "MODEL_ID = \"mlabonne/EvolCodeLlama-7b\"\n", + "QUANTIZATION_METHODS = [\"q4_k_m\", \"q5_k_m\"]\n", + "\n", + "# Constants\n", + "MODEL_NAME = MODEL_ID.split('/')[-1]\n", + "\n", + "# Install llama.cpp\n", + "!git clone https://github.com/ggerganov/llama.cpp\n", + "!cd llama.cpp && git pull && make clean && LLAMA_CUBLAS=1 make\n", + "!pip install -r llama.cpp/requirements.txt\n", + "\n", + "# Download model\n", + "!git lfs install\n", + "!git clone https://huggingface.co/{MODEL_ID}\n", + "\n", + "# Convert to fp16\n", + "fp16 = f\"{MODEL_NAME}/{MODEL_NAME.lower()}.fp16.bin\"\n", + "!python llama.cpp/convert.py {MODEL_NAME} --outtype f16 --outfile {fp16}" ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "## Run inference\n", + "## Important note to clear disk.\n", "\n", - "Here is a simple script to run your quantized models. I'm offloading every layer to the GPU (35 for a 7b parameter model) to speed up inference." - ], - "metadata": { - "id": "WqI1CPiXI4dP" - } + "As current Google Colab free tier instance gives you 107GB of Disk, most downloaded models will fillup this storage at this step. For solving this problem, you can either:\n", + "\n", + "* Delete the \".safetensors\" files in the \"models\" folder because they are not needed for quantization.\n", + "* Do not delete file that ends up with extension .bin as it will be used in next steps." + ] }, { - "cell_type": "code", + "cell_type": "markdown", + "metadata": {}, "source": [ - "import os\n", + "## Install CUDA with version 10.0\n", "\n", - "model_list = [file for file in os.listdir(MODEL_NAME) if \"gguf\" in file]\n", + "Installing CUDA with version 10.0 is needed to solve \"quantize\" script's common errors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# install cuda 10.0 as its the only problem with \n", + "!apt-get update;\n", + "!wget https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda-repo-ubuntu1604-10-0-local-10.0.130-410.48_1.0-1_amd64 -O cuda-repo-ubuntu1604-10-0-local-10.0.130-410.48_1.0-1_amd64.deb\n", + "!dpkg -i cuda-repo-ubuntu1604-10-0-local-10.0.130-410.48_1.0-1_amd64.deb\n", + "!apt-key add /var/cuda-repo-10-0-local/7fa2af80.pub\n", + "!apt-get update\n", + "!apt-get -y install gcc-7 g++-7\n", + "!apt-get -y install cuda\n", "\n", - "prompt = input(\"Enter your prompt: \")\n", - "chosen_method = input(\"Name of the model (options: \" + \", \".join(model_list) + \"): \")\n", + "!export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}\n", + "!export LD_LIBRARY_PATH=/usr/local/cuda/lib64\\${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}\n", "\n", - "# Verify the chosen method is in the list\n", - "if chosen_method not in model_list:\n", - " print(\"Invalid name\")\n", - "else:\n", + "# check if installed successfully\n", + "!/usr/local/cuda/bin/nvcc --version" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Start quntization process" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a script to train quantized versions of your provided model at the beginning." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Quantize the model for each method in the QUANTIZATION_METHODS list\n", + "for method in QUANTIZATION_METHODS:\n", " qtype = f\"{MODEL_NAME}/{MODEL_NAME.lower()}.{method.upper()}.gguf\"\n", - " !./llama.cpp/main -m {qtype} -n 128 --color -ngl 35 -p \"{prompt}\"" - ], + " !./llama.cpp/quantize {fp16} {qtype} {method}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WqI1CPiXI4dP" + }, + "source": [ + "## Run inference\n", + "\n", + "Here is a simple script to run your quantized models. I'm offloading every layer to the GPU (35 for a 7b parameter model) to speed up inference." + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1721,11 +789,10 @@ "id": "vNPL9WYg78l-", "outputId": "3c3e7d2f-f0de-429d-fd97-dab480bc514a" }, - "execution_count": null, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Enter your prompt: prompt\n", "Please specify the quantization method to run the model (options: q4_k_s): q4_k_s\n", @@ -2104,48 +1171,37 @@ "llama_print_timings: total time = 6370.96 ms\n" ] } + ], + "source": [ + "import os\n", + "\n", + "model_list = [file for file in os.listdir(MODEL_NAME) if \"gguf\" in file]\n", + "\n", + "prompt = input(\"Enter your prompt: \")\n", + "chosen_method = input(\"Name of the model (options: \" + \", \".join(model_list) + \"): \")\n", + "\n", + "# Verify the chosen method is in the list\n", + "if chosen_method not in model_list:\n", + " print(\"Invalid name\")\n", + "else:\n", + " qtype = f\"{MODEL_NAME}/{MODEL_NAME.lower()}.{method.upper()}.gguf\"\n", + " !./llama.cpp/main -m {qtype} -n 128 --color -ngl 35 -p \"{prompt}\"" ] }, { "cell_type": "markdown", + "metadata": { + "id": "Ar8pO7bb80US" + }, "source": [ "## Push to hub\n", "\n", "To push your model to the hub, you'll need to input your Hugging Face token (https://huggingface.co/settings/tokens) in Google Colab's \"Secrets\" tab. The following code creates a new repo with the \"-GGUF\" suffix. Don't forget to change the `username` variable." - ], - "metadata": { - "id": "Ar8pO7bb80US" - } + ] }, { "cell_type": "code", - "source": [ - "!pip install -q huggingface_hub\n", - "from huggingface_hub import create_repo, HfApi\n", - "from google.colab import userdata\n", - "\n", - "# Defined in the secrets tab in Google Colab\n", - "hf_token = userdata.get('huggingface')\n", - "\n", - "api = HfApi()\n", - "username = \"mlabonne\"\n", - "\n", - "# Create empty repo\n", - "create_repo(\n", - " repo_id = f\"{username}/{MODEL_NAME}-GGUF\",\n", - " repo_type=\"model\",\n", - " exist_ok=True,\n", - " token=hf_token\n", - ")\n", - "\n", - "# Upload gguf files\n", - "api.upload_folder(\n", - " folder_path=MODEL_NAME,\n", - " repo_id=f\"{username}/{MODEL_NAME}-GGUF\",\n", - " allow_patterns=f\"*.gguf\",\n", - " token=hf_token\n", - ")" - ], + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2185,34 +1241,1042 @@ "7f8e268db8144adfb09d089784d8411a" ] }, - "id": "UOyKfUD-8jmh", - "outputId": "3c8df47b-f350-4251-a19f-4b9fb1116381" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/268.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.7/268.8 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h" - ] + "id": "UOyKfUD-8jmh", + "outputId": "3c8df47b-f350-4251-a19f-4b9fb1116381" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/268.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.7/268.8 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c281b60e104f4c5da547bbdd7208d4bc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='

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