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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "JHRpOZ5g3Flv"
},
"source": [
"# Clone Mergekit and Install the dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "x8548KdSbMs2"
},
"outputs": [],
"source": [
"!nvidia-smi"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4alsYntU1gNU"
},
"outputs": [],
"source": [
"!pip install -qqq git+https://github.com/arcee-ai/mergekit.git"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DtGY8BAo3alb"
},
"source": [
"# Mergekit Config"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CmfbveTblP0F"
},
"outputs": [],
"source": [
"# @markdown What is your model's name will be?\n",
"MODEL_NAME = 'SmolMoE' # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "r2-rAjH93w8x"
},
"outputs": [],
"source": [
"mergekit_yaml = \"\"\"\n",
"base_model: BEE-spoke-data/smol_llama-220M-GQA\n",
"gate_mode: random\n",
"dtype: bfloat16\n",
"experts:\n",
" - source_model: BEE-spoke-data/smol_llama-220M-GQA\n",
" - source_model: BEE-spoke-data/smol_llama-220M-GQA\n",
"\"\"\" # @param {type:\"string\"}\n",
"with open('config.yaml', 'w', encoding=\"utf-8\") as f:\n",
" f.write(mergekit_yaml)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WiCGZXysn_mD"
},
"source": [
"# Mergekit Runtime"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0scr7Ed_4GPe"
},
"outputs": [],
"source": [
"low_cpu_ram = True # @param {type:\"boolean\"}\n",
"runtime = \"GPU\" # @param [\"CPU\", \"GPU\"]\n",
"task = \"merge-mega\" # @param [\"merge\", \"merge-mega\", \"moe\", \"extract\"]\n",
"# @markdown ### Mergekit arguments\n",
"\n",
"trust_remote_code = False # @param {type:\"boolean\"}\n",
"clone_tensors = True # @param {type:\"boolean\"}\n",
"low_ram = True # @param {type:\"boolean\"}\n",
"out_shard_size = \"500M\" # @param {type:\"string\"}\n",
"\n",
"# @markdown ### Extract LoRA (experimental)\n",
"base_model = \"unsloth/Llama-3.2-3B-Instruct\" # @param {type:\"string\"}\n",
"finetuned_model = \"theprint/ReWiz-Llama-3.2-3B\" # @param {type:\"string\"}\n",
"extract_rank = 32 # @param {type:\"number\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QBhBgX7U52Xn"
},
"source": [
"## Run the program"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"id": "3Y7aBJXL54GJ"
},
"outputs": [],
"source": [
"import os\n",
"import shutil\n",
"\n",
"def empty_folder(folder_path):\n",
" if os.path.exists(folder_path):\n",
" shutil.rmtree(folder_path)\n",
" os.makedirs(folder_path)\n",
"\n",
"empty_folder('merge')\n",
"empty_folder('lora')\n",
"\n",
"if task == \"merge\":\n",
" cli = \"mergekit-yaml\"\n",
"elif task == \"merge-mega\":\n",
" cli = \"mergekit-mega\"\n",
"elif task == \"moe\":\n",
" cli = \"mergekit-moe\"\n",
"elif task == \"extract\":\n",
" if base_model == \"\" or finetuned_model == \"\":\n",
" raise ValueError(\"base_model and finetuned_model cannot be empty\")\n",
" !pip install -qqq bitsandbytes\n",
" cli = f\"mergekit-extract-lora {finetuned_model} {base_model} lora --rank={extract_rank}\"\n",
"\n",
"if task in [\"merge\", \"moe\", \"merge-mega\"]:\n",
" cli += \" config.yaml merge --copy-tokenizer --allow-crimes\"\n",
" if runtime == \"GPU\":\n",
" if task in [\"merge\", \"merge-mega\"]:\n",
" cli += \" --cuda\"\n",
" elif task == \"moe\":\n",
" cli += \" --device cuda --cuda\"\n",
" else:\n",
" cli += \" --no-cuda\"\n",
"\n",
" if trust_remote_code:\n",
" cli += \" --trust-remote-code\"\n",
" if clone_tensors:\n",
" cli += \" --clone-tensors\"\n",
" if low_ram:\n",
" cli += f\" --out-shard-size {out_shard_size} --lazy-unpickle\"\n",
" if low_cpu_ram:\n",
" cli += \" --low-cpu-memory\"\n",
"print(cli)\n",
"!{cli}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HyeGrtGrDn6S"
},
"source": [
"# Inference the Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wpy7Ahw6hghH"
},
"outputs": [],
"source": [
"!pip install -qU transformers bitsandbytes accelerate\n",
"from transformers import AutoTokenizer, pipeline\n",
"import torch\n",
"\n",
"model = \"merge\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model)\n",
"generator = pipeline(\n",
" \"text-generation\",\n",
" model=model,\n",
" model_kwargs={\"torch_dtype\": torch.float16, \"load_in_4bit\": False},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "f05D7q8wiF-5"
},
"outputs": [],
"source": [
"messages = [{\"role\": \"user\", \"content\": \"Explain what a Mixture of Experts is in less than 100 words.\"}]\n",
"prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
"outputs = generator(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)\n",
"print(outputs[0][\"generated_text\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Upload to Hugging Face"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @title ## Upload model to Hugging Face { display-mode: \"form\" }\n",
"# @markdown Enter your HF username and the name of Colab secret that stores your [Hugging Face access token](https://huggingface.co/settings/tokens).\n",
"username = 'username' # @param {type:\"string\"}\n",
"token_env = 'hf_token' # @param {type:\"string\"}\n",
"\n",
"!pip install -qU huggingface_hub\n",
"\n",
"import os\n",
"import yaml\n",
"\n",
"from huggingface_hub import HfApi\n",
"from google.colab import userdata\n",
"\n",
"def output_dir():\n",
" if os.path.exists('merge') and os.listdir('merge'):\n",
" return \"merge\"\n",
" if os.path.exists('lora') and os.listdir('lora'):\n",
" return \"lora\"\n",
" raise ValueError(\"Both folders are empty or do not exist.\")\n",
"\n",
"\n",
"# Defined in the secrets tab in Google Colab\n",
"api = HfApi(token=userdata.get(token_env))\n",
"try:\n",
" output_dir=output_dir()\n",
" api.create_repo(\n",
" repo_id=f\"{username}/{MODEL_NAME}\",\n",
" repo_type=\"model\",\n",
" exist_ok=True,\n",
" )\n",
" api.upload_folder(\n",
" repo_id=f\"{username}/{MODEL_NAME}\",\n",
" folder_path=output_dir,\n",
" )\n",
"except ValueError as e:\n",
" print(e)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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