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1770 lines
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"# Merge Large Language Models with mergekit\n",
|
||
"> 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course)\n",
|
||
"\n",
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"❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne).\n",
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"\n",
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||
"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",
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||
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|
||
"\n",
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||
"### TIES-Merging\n",
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"\n",
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"```yaml\n",
|
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"models:\n",
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||
" - model: mistralai/Mistral-7B-v0.1\n",
|
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" # no parameters necessary for base model\n",
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" - model: OpenPipe/mistral-ft-optimized-1218\n",
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" density: 0.5\n",
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" weight: 0.3\n",
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|
||
"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",
|
||
"9b48494c94cf49b5835489d97f7a24c5",
|
||
"6ed844da52fe466eb1c10c814489448c",
|
||
"9c60efa02e80423e828628190dd13bc3",
|
||
"0170e8cc57d94041956f7afbf2eef449",
|
||
"220c2ba5f2524271b24fe049431a474c",
|
||
"a6f99dd0662846f9a381d2d507a7b447",
|
||
"900b9fcb70a84781bd5b4213df54626d",
|
||
"0ea83f270e164795b64f23b143efb300",
|
||
"318dcdeac8fb40f88fa60114f1c6a7c1",
|
||
"af89cf715e0e4c5e9f59943a255394c1",
|
||
"40e23e35299d45d499432b8f1a9bc924",
|
||
"126b374e286747768ef7218454534640",
|
||
"bdd26e54eed5477f99b135552e5f3450",
|
||
"163a6fd878134e1eb5f193d1ebfff1c1",
|
||
"953d7c014f76413c9805a2ef8c2c9356",
|
||
"348879bf76d1471f9c79c1ec2dc07c1d",
|
||
"8d54ae0d028b40e7b018454187db1a1c",
|
||
"562353040be54593b23734390f49927c",
|
||
"00cbebe6df7d4995913f20e39fc71b15",
|
||
"aee3c563fdc54f9cb3ebc2630c84a9e6",
|
||
"b74e307a751844ffab9f7f3df367774b",
|
||
"8e6142e41f714fe9abe6a5bb72c071f9",
|
||
"49cd1c5663404fb5a307c345e7e970c3",
|
||
"920ef8e509d24ccda930f4c47eff158c",
|
||
"c8828d61b26a47ac97a1541e14c00f62",
|
||
"a3d7e352222647a99be79935b1ebd86a",
|
||
"80666ef5f07641c482a23618a767791d"
|
||
]
|
||
},
|
||
"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]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "de24d272f2b842c5b01eedb3f536b810"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"model-00001-of-00002.safetensors: 0%| | 0.00/9.94G [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "63626ac2d0f546188c07512a04c71417"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Upload 3 LFS files: 0%| | 0/3 [00:00<?, ?it/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "a6f99dd0662846f9a381d2d507a7b447"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"model-00002-of-00002.safetensors: 0%| | 0.00/8.03G [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "8d54ae0d028b40e7b018454187db1a1c"
|
||
}
|
||
},
|
||
"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
|
||
}
|
||
]
|
||
}
|
||
]
|
||
} |