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"<a href=\"https://colab.research.google.com/github/mlabonne/llm-course/blob/main/Mergekit.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"# Merge Large Language Models with mergekit\n",
"> 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course)\n",
"\n",
"❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne).\n",
"\n",
"Model merging only requires a lot of RAM. With a free Google Colab account, you should be able to run it using a T4 GPU (VRAM offloading).\n",
"\n",
"Examples of merge configurations:\n",
"\n",
"### TIES-Merging\n",
"\n",
"```yaml\n",
"models:\n",
" - model: mistralai/Mistral-7B-v0.1\n",
" # no parameters necessary for base model\n",
" - model: OpenPipe/mistral-ft-optimized-1218\n",
" parameters:\n",
" density: 0.5\n",
" weight: 0.5\n",
" - model: mlabonne/NeuralHermes-2.5-Mistral-7B\n",
" parameters:\n",
" density: 0.5\n",
" weight: 0.3\n",
"merge_method: ties\n",
"base_model: mistralai/Mistral-7B-v0.1\n",
"parameters:\n",
" normalize: true\n",
"dtype: float16\n",
"```\n",
"\n",
"You can find the final model on the Hugging Face Hub at [mlabonne/NeuralPipe-7B-ties](https://huggingface.co/mlabonne/NeuralPipe-7B-ties).\n",
"\n",
"### SLERP\n",
"\n",
"```yaml\n",
"slices:\n",
" - sources:\n",
" - model: OpenPipe/mistral-ft-optimized-1218\n",
" layer_range: [0, 32]\n",
" - model: mlabonne/NeuralHermes-2.5-Mistral-7B\n",
" layer_range: [0, 32]\n",
"merge_method: slerp\n",
"base_model: OpenPipe/mistral-ft-optimized-1218\n",
"parameters:\n",
" t:\n",
" - filter: self_attn\n",
" value: [0, 0.5, 0.3, 0.7, 1]\n",
" - filter: mlp\n",
" value: [1, 0.5, 0.7, 0.3, 0]\n",
" - value: 0.5\n",
"dtype: bfloat16\n",
"```\n",
"\n",
"You can find the final model on the Hugging Face Hub at [mlabonne/NeuralPipe-7B-slerp](https://huggingface.co/mlabonne/NeuralPipe-7B-slerp).\n",
"\n",
"### Passthrough\n",
"\n",
"```yaml\n",
"slices:\n",
" - sources:\n",
" - model: OpenPipe/mistral-ft-optimized-1218\n",
" layer_range: [0, 32]\n",
" - sources:\n",
" - model: mlabonne/NeuralHermes-2.5-Mistral-7B\n",
" layer_range: [24, 32]\n",
"merge_method: passthrough\n",
"dtype: bfloat16\n",
"```\n",
"\n",
"You can find the final model on the Hugging Face Hub at [mlabonne/NeuralPipe-9B-merged](https://huggingface.co/mlabonne/NeuralPipe-9B-merged)."
],
"metadata": {
"id": "o12O0YjJvvLW"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NPNPie5Eo3EZ"
},
"outputs": [],
"source": [
"!git clone https://github.com/cg123/mergekit.git\n",
"!cd mergekit && pip install -q -e ."
]
},
{
"cell_type": "code",
"source": [
"import yaml\n",
"\n",
"MODEL_NAME = \"Marcoro14-7B-slerp\"\n",
"yaml_config = \"\"\"\n",
"slices:\n",
" - sources:\n",
" - model: AIDC-ai-business/Marcoroni-7B-v3\n",
" layer_range: [0, 32]\n",
" - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1\n",
" layer_range: [0, 32]\n",
"merge_method: slerp\n",
"base_model: AIDC-ai-business/Marcoroni-7B-v3\n",
"parameters:\n",
" t:\n",
" - filter: self_attn\n",
" value: [0, 0.5, 0.3, 0.7, 1]\n",
" - filter: mlp\n",
" value: [1, 0.5, 0.7, 0.3, 0]\n",
" - value: 0.5\n",
"dtype: bfloat16\n",
"\n",
"\"\"\"\n",
"\n",
"# Save config as yaml file\n",
"with open('config.yaml', 'w', encoding=\"utf-8\") as f:\n",
" f.write(yaml_config)"
],
"metadata": {
"id": "LGd7jlfCpNcg"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Merge models\n",
"!mergekit-yaml config.yaml merge --copy-tokenizer --allow-crimes --out-shard-size 1B --lazy-unpickle"
],
"metadata": {
"id": "d5mYzDo1q96y"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip install -qU huggingface_hub\n",
"\n",
"from huggingface_hub import ModelCard, ModelCardData\n",
"from jinja2 import Template\n",
"\n",
"username = \"mlabonne\"\n",
"\n",
"template_text = \"\"\"\n",
"---\n",
"license: apache-2.0\n",
"tags:\n",
"- merge\n",
"- mergekit\n",
"- lazymergekit\n",
"{%- for model in models %}\n",
"- {{ model }}\n",
"{%- endfor %}\n",
"---\n",
"\n",
"# {{ model_name }}\n",
"\n",
"{{ model_name }} is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):\n",
"\n",
"{%- for model in models %}\n",
"* [{{ model }}](https://huggingface.co/{{ model }})\n",
"{%- endfor %}\n",
"\n",
"## 🧩 Configuration\n",
"\n",
"```yaml\n",
"{{- yaml_config -}}\n",
"```\n",
"\"\"\"\n",
"\n",
"# Create a Jinja template object\n",
"jinja_template = Template(template_text.strip())\n",
"\n",
"# Get list of models from config\n",
"data = yaml.safe_load(yaml_config)\n",
"if \"models\" in data:\n",
" models = [data[\"models\"][i][\"model\"] for i in range(len(data[\"models\"])) if \"parameters\" in data[\"models\"][i]]\n",
"elif \"parameters\" in data:\n",
" models = [data[\"slices\"][0][\"sources\"][i][\"model\"] for i in range(len(data[\"slices\"][0][\"sources\"]))]\n",
"elif \"slices\" in data:\n",
" models = [data[\"slices\"][i][\"sources\"][0][\"model\"] for i in range(len(data[\"slices\"]))]\n",
"else:\n",
" raise Exception(\"No models or slices found in yaml config\")\n",
"\n",
"# Fill the template\n",
"content = jinja_template.render(\n",
" model_name=MODEL_NAME,\n",
" models=models,\n",
" yaml_config=yaml_config,\n",
" username=username,\n",
")\n",
"\n",
"# Save the model card\n",
"card = ModelCard(content)\n",
"card.save('merge/README.md')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "w-RNKev373lI",
"outputId": "fccbbd1d-295f-4def-a398-f226813294bb"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/330.1 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[32m112.6/330.1 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m330.1/330.1 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import userdata\n",
"from huggingface_hub import HfApi\n",
"\n",
"username = \"mlabonne\"\n",
"\n",
"# Defined in the secrets tab in Google Colab\n",
"api = HfApi(token=userdata.get(\"HF_TOKEN\"))\n",
"\n",
"api.create_repo(\n",
" repo_id=f\"{username}/{MODEL_NAME}\",\n",
" repo_type=\"model\"\n",
")\n",
"api.upload_folder(\n",
" repo_id=f\"{username}/{MODEL_NAME}\",\n",
" folder_path=\"merge\",\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 164,
"referenced_widgets": [
"de24d272f2b842c5b01eedb3f536b810",
"0c5dab2657b2473385a424d90f3d4664",
"57efe36e546c473d8be34102f6ba9a58",
"871bad1d905d4877a9eaa242cfd54c4e",
"8951f6b2edf64464869391197c900f84",
"69a61ad28d5141dcbaea44060bc5ebf7",
"76c2fbf005ae4a5790edfeb499b387b7",
"116964f328dc45d991d895d684ac1216",
"1ecec5ba4424498082a5f64cf3d7faf8",
"fc4edcef273b4e75894f4b512122de94",
"ca2323b142f54998985d30481d5cfabe",
"63626ac2d0f546188c07512a04c71417",
"decd91747fd04ce39f3e2b733bc7f477",
"7140e4c154424fcab846a71889e99ed2",
"2264d8b75251425e94e635558af4e223",
"c37478198217457cb30c6649203cf4dc",
"4918769e4e984dfda924776e2373154c",
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"126b374e286747768ef7218454534640",
"bdd26e54eed5477f99b135552e5f3450",
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]
},
"id": "ik0V0dF55gfU",
"outputId": "9f6c605c-6b51-473d-c1fa-b103e9208785"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer.model: 0%| | 0.00/493k [00:00<?, ?B/s]"
],
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},
{
"output_type": "display_data",
"data": {
"text/plain": [
"model-00001-of-00002.safetensors: 0%| | 0.00/9.94G [00:00<?, ?B/s]"
],
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}
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},
{
"output_type": "display_data",
"data": {
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"Upload 3 LFS files: 0%| | 0/3 [00:00<?, ?it/s]"
],
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{
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],
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"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'https://huggingface.co/mlabonne/NeuralPipe-9B-merged/tree/main/'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 5
}
]
}
]
}
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