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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/almutareb/InnovationPathfinderAI/blob/main/%F0%9F%A4%96_AutoQuantize_(GGUF%2C_AWQ%2C_EXL2%2C_GPTQ).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"# @title # 🤖 AutoQuantize\n",
"\n",
"# @markdown 🔮 Created by [@zainulabideen](https://huggingface.co/abideen).\n",
"\n",
"# @markdown Please add HF token to the secrets tab in Google Colab before.\n",
"\n",
"# @markdown Quantization formats supported: `GGUF`, `AWQ`, `EXL2`, `GPTQ`\n",
"\n",
"# @markdown ---\n",
"\n",
"\n",
"# @markdown ### 🤗 Hugging Face Hub\n",
"\n",
"MODEL_ID = \"abideen/Heimer-dpo-TinyLlama-1.1B\" # @param {type:\"string\"}\n",
"MODEL_NAME = MODEL_ID.split('/')[-1]\n",
"\n",
"# Download model\n",
"!git lfs install\n",
"!git clone https://huggingface.co/{MODEL_ID}\n",
"\n",
"username = \"abideen\" # @param {type:\"string\"}\n",
"token = \"\" # @param {type:\"string\"}\n",
"!pip install -q huggingface_hub\n",
"from huggingface_hub import create_repo, HfApi\n",
"from google.colab import userdata, runtime"
],
"metadata": {
"id": "fD24jJxq7t3k",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# @title # 🛸 GGUF\n",
"# @markdown ### ✨ Quantization parameters\n",
"\n",
"QUANTIZATION_FORMAT = \"q4_k_m\" # @param {type:\"string\"}\n",
"QUANTIZATION_METHODS = QUANTIZATION_FORMAT.replace(\" \", \"\").split(\",\")\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",
"# 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}\n",
"\n",
"# Defined in the secrets tab in Google Colab\n",
"hf_token = userdata.get(token)\n",
"api = HfApi()\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=[\"*.gguf\",\"$.md\"],\n",
" token=hf_token\n",
")"
],
"metadata": {
"id": "NL0yGhbe3EFk",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# @title # 🏛️ AWQ\n",
"# @markdown ### ✨ Quantization parameters\n",
"\n",
"Q_GROUP_SIZE = 128 # @param {type:\"integer\"}\n",
"ZERO_POINT = True # @param {text:\"boolean\"}\n",
"W_BIT = 4 # @param {type:\"integer\"}\n",
"VERSION = \"GEMM\" # @param {type:\"string\"}\n",
"SAFETENSORS = True # @param {text:\"boolean\"}\n",
"\n",
"# Install AutoAWQ\n",
"!git clone https://github.com/casper-hansen/AutoAWQ\n",
"%cd AutoAWQ\n",
"!pip install -e .\n",
"!pip install git+https://github.com/huggingface/transformers\n",
"!pip install zstandard\n",
"\n",
"from awq import AutoAWQForCausalLM\n",
"from transformers import AutoTokenizer\n",
"\n",
"\n",
"quant_path = MODEL_NAME + \"-awq\"\n",
"quant_config = { \"zero_point\": ZERO_POINT, \"q_group_size\": Q_GROUP_SIZE, \"w_bit\": W_BIT, \"version\": VERSION }\n",
"\n",
"# Load model\n",
"PATH = \"/content/\" + MODEL_NAME\n",
"model = AutoAWQForCausalLM.from_pretrained(PATH, safetensors=SAFETENSORS)\n",
"tokenizer = AutoTokenizer.from_pretrained(PATH, trust_remote_code=True)\n",
"\n",
"# Quantize\n",
"model.quantize(tokenizer, quant_config=quant_config)\n",
"\n",
"# Save quantized model\n",
"model.save_quantized(quant_path)\n",
"tokenizer.save_pretrained(quant_path)\n",
"\n",
"# Defined in the secrets tab in Google Colab\n",
"hf_token = userdata.get(token)\n",
"api = HfApi()\n",
"\n",
"# Create empty repo\n",
"create_repo(\n",
" repo_id = f\"{username}/{MODEL_NAME}-AWQ\",\n",
" repo_type=\"model\",\n",
" exist_ok=True,\n",
" token=hf_token\n",
")\n",
"\n",
"# Upload awq files\n",
"api.upload_folder(\n",
" folder_path=quant_path,\n",
" repo_id=f\"{username}/{MODEL_NAME}-AWQ\",\n",
" token=hf_token\n",
")"
],
"metadata": {
"id": "MyyUO2Fj3WHt",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# @title # 🔬 EXL2\n",
"# @markdown ### ✨ Quantization parameters\n",
"\n",
"BPW = 5.0 # @param {type:\"number\"}\n",
"\n",
"# Install ExLLamaV2\n",
"!git clone https://github.com/turboderp/exllamav2\n",
"!pip install -e exllamav2\n",
"\n",
"!mv {MODEL_NAME} base_model\n",
"!rm base_mode/*.bin\n",
"\n",
"# Download dataset\n",
"!wget https://huggingface.co/datasets/wikitext/resolve/9a9e482b5987f9d25b3a9b2883fc6cc9fd8071b3/wikitext-103-v1/wikitext-test.parquet\n",
"\n",
"# Quantize model\n",
"!mkdir quant\n",
"!python exllamav2/convert.py \\\n",
" -i base_model \\\n",
" -o quant \\\n",
" -c wikitext-test.parquet \\\n",
" -b {BPW}\n",
"\n",
"# Copy files\n",
"!rm -rf quant/out_tensor\n",
"!rsync -av --exclude='*.safetensors' --exclude='.*' ./base_model/ ./quant/\n",
"\n",
"# Defined in the secrets tab in Google Colab\n",
"hf_token = userdata.get(token)\n",
"api = HfApi()\n",
"\n",
"# Create empty repo\n",
"create_repo(\n",
" repo_id = f\"{username}/{MODEL_NAME}-{BPW:.1f}bpw-exl2\",\n",
" repo_type=\"model\",\n",
" exist_ok=True,\n",
" token=hf_token\n",
")\n",
"\n",
"# Upload exl2 files\n",
"api.upload_folder(\n",
" folder_path=quant,\n",
" repo_id=f\"{username}/{MODEL_NAME}-{BPW:.1f}bpw-exl2\",\n",
" token=hf_token\n",
")"
],
"metadata": {
"id": "ZC9Nsr9u5WhN",
"cellView": "form"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# @title # 📝 GPTQ\n",
"# @markdown ### ✨ Quantization parameters\n",
"\n",
"BITS = 4 # @param {type:\"integer\"}\n",
"GROUP_SIZE = 128 # @param {type:\"integer\"}\n",
"DAMP_PERCENT = 0.01 # @param {type:\"number\"}\n",
"\n",
"!BUILD_CUDA_EXT=0 pip install -q auto-gptq transformers\n",
"import random\n",
"from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig\n",
"from datasets import load_dataset\n",
"import torch\n",
"from transformers import AutoTokenizer\n",
"out_dir = MODEL_ID + \"-GPTQ\"\n",
"\n",
"# Load quantize config, model and tokenizer\n",
"quantize_config = BaseQuantizeConfig(\n",
" bits=BITS,\n",
" group_size=GROUP_SIZE,\n",
" damp_percent=DAMP_PERCENT,\n",
" desc_act=False,\n",
")\n",
"PATH = \"/content/\" + MODEL_NAME\n",
"model = AutoGPTQForCausalLM.from_pretrained(PATH, quantize_config)\n",
"tokenizer = AutoTokenizer.from_pretrained(PATH)\n",
"\n",
"# Load data and tokenize examples\n",
"n_samples = 1024\n",
"data = load_dataset(\"allenai/c4\", data_files=\"en/c4-train.00001-of-01024.json.gz\", split=f\"train[:{n_samples*5}]\")\n",
"tokenized_data = tokenizer(\"\\n\\n\".join(data['text']), return_tensors='pt')\n",
"\n",
"# Format tokenized examples\n",
"examples_ids = []\n",
"for _ in range(n_samples):\n",
" i = random.randint(0, tokenized_data.input_ids.shape[1] - tokenizer.model_max_length - 1)\n",
" j = i + tokenizer.model_max_length\n",
" input_ids = tokenized_data.input_ids[:, i:j]\n",
" attention_mask = torch.ones_like(input_ids)\n",
" examples_ids.append({'input_ids': input_ids, 'attention_mask': attention_mask})\n",
"\n",
"# Quantize with GPTQ\n",
"model.quantize(\n",
" examples_ids,\n",
" batch_size=1,\n",
" use_triton=True,\n",
")\n",
"\n",
"# Save model and tokenizer\n",
"model.save_quantized(out_dir, use_safetensors=True)\n",
"tokenizer.save_pretrained(out_dir)\n",
"\n",
"# Defined in the secrets tab in Google Colab\n",
"hf_token = userdata.get(token)\n",
"api = HfApi()\n",
"\n",
"# Create empty repo\n",
"create_repo(\n",
" repo_id = f\"{username}/{MODEL_NAME}-GPTQ\",\n",
" repo_type=\"model\",\n",
" exist_ok=True,\n",
" token=hf_token\n",
")\n",
"\n",
"# Upload gptq files\n",
"api.upload_folder(\n",
" folder_path=out_dir,\n",
" repo_id=f\"{username}/{MODEL_NAME}-GPTQ\",\n",
" token=hf_token\n",
")\n"
],
"metadata": {
"id": "OE_R3AXG5Y-F",
"cellView": "form"
},
"execution_count": null,
"outputs": []
}
]
}
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