Upload Imat_AutoGGUF.ipynb
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Imat_AutoGGUF.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"gpuType": "T4"
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "code",
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"source": [
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"# @title # ⚡ Imat-AutoGGUF\n",
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"\n",
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"# @markdown Made by https://huggingface.co/Virt-io\n",
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"\n",
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"# @markdown Edited https://github.com/mlabonne/llm-course LazyMergekit to work with Imatrix\n",
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"\n",
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"# @markdown\n",
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"\n",
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"# @markdown The `token` corresponds to the name of the secret that stores your [Hugging Face access token](https://huggingface.co/settings/tokens) in Colab.\n",
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"\n",
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"# @markdown ---\n",
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"\n",
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"# @markdown ### ⚡ Quantization parameters\n",
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"MODEL_ID = \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\" # @param {type:\"string\"}\n",
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"IMATRIX_OPTION = 'Imatrix' # @param [\"Imatrix\", \"Imatrix-RP\"]\n",
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"if IMATRIX_OPTION == \"Imatrix\":\n",
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" IMATRIX = f\"Google-Colab-Imatrix-GGUF/Imatrix/imatrix.txt\"\n",
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"if IMATRIX_OPTION == \"Imatrix-RP\":\n",
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" IMATRIX = f\"Google-Colab-Imatrix-GGUF/Imatrix/imatrix-with-rp-data.txt\"\n",
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"print(IMATRIX)\n",
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"QUANTIZATION_METHODS = \"IQ4_NL, Q8_0\" # @param {type:\"string\"}\n",
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"QUANTIZATION_METHODS = QUANTIZATION_METHODS.replace(\" \", \"\").split(\",\")\n",
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"\n",
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"# @markdown ---\n",
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"\n",
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"# @markdown ### 🤗 Hugging Face Hub\n",
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"username = \"Virt-io\" # @param {type:\"string\"}\n",
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"token = \"HF_TOKEN\" # @param {type:\"string\"}\n",
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"\n",
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"MODEL_NAME = MODEL_ID.split('/')[-1]\n",
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"\n",
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"# Git clone llamacpp\n",
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"!git clone https://github.com/ggerganov/llama.cpp\n",
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"!cd llama.cpp && git pull\n",
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"\n",
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"# Download model\n",
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"!git lfs install\n",
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"!git clone https://huggingface.co/{MODEL_ID}\n",
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"\n",
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"# Download Imatrix\n",
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"!git clone https://huggingface.co/Virt-io/Google-Colab-Imatrix-GGUF\n",
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"\n",
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"# Install python dependencies and reload instance\n",
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"!pip install -r llama.cpp/requirements/requirements-convert.txt\n",
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"\n",
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"# Build llamacpp\n",
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"!cd llama.cpp && make clean && LLAMA_CUBLAS=1 LLAMA_CUDA_FORCE_MMQ=1 LLAMA_LTO=1 LLAMA_CUDA_DMMV_X=64 LLAMA_CUDA_MMV_Y=4 LLAMA_CUDA_KQUANTS_ITER=2 LLAMA_CUDA_F16=1 LLAMA_CUDA_DMMV_F16=1 make -j16\n",
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"\n",
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"# Convert to fp16\n",
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"fp16 = f\"{MODEL_NAME}/{MODEL_NAME.lower()}.fp16.gguf\"\n",
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"!python llama.cpp/convert.py {MODEL_NAME} --outtype f16 --outfile {fp16}\n",
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"\n",
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"# Run imatrix\n",
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"imat_dat = f\"{fp16}.{IMATRIX_OPTION}.dat\"\n",
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"!./llama.cpp/imatrix -ngl 100 -c 512 -b 512 --model {fp16} -f {IMATRIX} -o {imat_dat}\n",
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"\n",
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"# Quantize the model for each method in the QUANTIZATION_METHODS list\n",
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"for method in QUANTIZATION_METHODS:\n",
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" qtype = f\"{MODEL_NAME}/{MODEL_NAME.lower()}.{method.upper()}.gguf\"\n",
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" !./llama.cpp/quantize --imatrix {imat_dat} {fp16} {qtype} {method}"
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],
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"metadata": {
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"id": "fD24jJxq7t3k"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# @markdown Upload to HF\n",
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"!pip install -q huggingface_hub\n",
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"from huggingface_hub import create_repo, HfApi\n",
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"from google.colab import userdata, runtime\n",
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"\n",
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"# Defined in the secrets tab in Google Colab\n",
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"hf_token = userdata.get(token)\n",
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"api = HfApi()\n",
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"\n",
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"# Create empty repo\n",
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"create_repo(\n",
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" repo_id = f\"{username}/{MODEL_NAME}-GGUF\",\n",
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" repo_type=\"model\",\n",
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" exist_ok=True,\n",
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" token=hf_token\n",
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")\n",
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"\n",
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"# Upload gguf files\n",
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"api.upload_folder(\n",
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" folder_path=MODEL_NAME,\n",
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" repo_id=f\"{username}/{MODEL_NAME}-GGUF\",\n",
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" allow_patterns=[\"*.gguf\", \"*.fp16.gguf\", \"*.dat\", \"*.md\"],\n",
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" token=hf_token\n",
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")\n",
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"\n",
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"# Kill runtime\n",
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"# runtime.unassign()"
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],
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"metadata": {
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"id": "F7Q8_Y1_e3BX"
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},
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"execution_count": null,
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"outputs": []
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}
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]
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}
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