LazierMergekit.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jankovicsandras/ml/blob/main/LazierMergekit.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🥱😴 LazierMergekit\n",
        "\n",
        "> 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course)\n",
        "\n",
        "❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne).\n",
        "\n",
        "This notebook allows you to easily merge multiple models using [mergekit](https://github.com/cg123/mergekit). To evaluate your merges, see [🧐 LLM AutoEval](https://colab.research.google.com/drive/1Igs3WZuXAIv9X0vwqiE90QlEPys8e8Oa?usp=sharing#scrollTo=elyxjYI_rY5W).\n",
        "\n",
        "Added [Llama.cpp](https://github.com/ggerganov/llama.cpp) quantization.\n",
        "\n",
        "*Special thanks to [@cg123](https://github.com/cg123) for this library and [@mrfakename](https://gist.github.com/fakerybakery) who told me about sharding (see his [Gist](https://gist.github.com/fakerybakery/d30a4d31b4f914757c1381166b9c683b)).*"
      ],
      "metadata": {
        "id": "1Wq4SB9A_9ic"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_NAME = \"Mindentural-7B-240124\"\n",
        "yaml_config = \"\"\"\n",
        "slices:\n",
        "  - sources:\n",
        "      - model: OpenPipe/mistral-ft-optimized-1218\n",
        "        layer_range: [0, 32]\n",
        "      - model: HyperbeeAI/Tulpar-7b-v2\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",
        "\"\"\""
      ],
      "metadata": {
        "id": "LGd7jlfCpNcg"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ## Run merge\n",
        "\n",
        "# @markdown ### Runtime type\n",
        "# @markdown Select your runtime (CPU, High RAM, GPU)\n",
        "\n",
        "runtime = \"CPU\" # @param [\"CPU\", \"CPU + High-RAM\", \"GPU\"]\n",
        "\n",
        "# @markdown ### Mergekit arguments\n",
        "# @markdown Use the `main` branch by default, [`mixtral`](https://github.com/cg123/mergekit/blob/mixtral/moe.md) if you want to create a Mixture of Experts.\n",
        "\n",
        "branch = \"main\" # @param [\"main\", \"mixtral\"]\n",
        "trust_remote_code = False # @param {type:\"boolean\"}\n",
        "\n",
        "# Install mergekit\n",
        "if branch == \"main\":\n",
        "    !git clone https://github.com/cg123/mergekit.git\n",
        "    !cd mergekit && pip install -qqq -e . --progress-bar off\n",
        "elif branch == \"mixtral\":\n",
        "    !git clone -b mixtral https://github.com/cg123/mergekit.git\n",
        "    !cd mergekit && pip install -qqq -e . --progress-bar off\n",
        "    !pip install -qqq -U transformers --progress-bar off\n",
        "\n",
        "# Save config as yaml file\n",
        "with open('config.yaml', 'w', encoding=\"utf-8\") as f:\n",
        "    f.write(yaml_config)\n",
        "\n",
        "# Base CLI\n",
        "if branch == \"main\":\n",
        "    cli = \"mergekit-yaml config.yaml merge --copy-tokenizer\"\n",
        "elif branch == \"mixtral\":\n",
        "    cli = \"mergekit-moe config.yaml merge --copy-tokenizer\"\n",
        "\n",
        "# Additional arguments\n",
        "if runtime == \"CPU\":\n",
        "    cli += \" --allow-crimes --out-shard-size 1B --lazy-unpickle\"\n",
        "elif runtime == \"GPU\":\n",
        "    cli += \" --cuda --low-cpu-memory\"\n",
        "if trust_remote_code:\n",
        "    cli += \" --trust-remote-code\"\n",
        "\n",
        "print(cli)\n",
        "\n",
        "# Merge models\n",
        "!{cli}"
      ],
      "metadata": {
        "id": "d5mYzDo1q96y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98e4681e-993c-4356-d93c-9f2d0453a7f1"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'mergekit' already exists and is not an empty directory.\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building editable for mergekit (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "mergekit-yaml config.yaml merge --copy-tokenizer --allow-crimes --out-shard-size 1B --lazy-unpickle\n",
            "Warmup loader cache:   0% 0/2 [00:00<?, ?it/s]\n",
            "Fetching 8 files: 100% 8/8 [00:00<00:00, 9675.44it/s]\n",
            "Warmup loader cache:  50% 1/2 [00:00<00:00,  6.21it/s]\n",
            "Fetching 8 files: 100% 8/8 [00:00<00:00, 41070.30it/s]\n",
            "Warmup loader cache: 100% 2/2 [00:00<00:00,  6.33it/s]\n",
            "100% 1457/1457 [09:01<00:00,  2.69it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf /root/.cache/huggingface/hub # IMPORTANT! Deleting the cached source models to free up space\n",
        "!ls -la\n",
        "!ls -la merge"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1c10c04-aea5-418c-a26b-8889673dce5d",
        "id": "YRwWx8ugnFjj"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 36\n",
            "drwxr-xr-x  1 root root 4096 Jan 24 13:09 .\n",
            "drwxr-xr-x  1 root root 4096 Jan 24 12:15 ..\n",
            "drwxr-xr-x  1 root root 4096 Jan 19 14:19 .config\n",
            "-rw-r--r--  1 root root  398 Jan 24 12:29 config.yaml\n",
            "drwxr-xr-x 21 root root 4096 Jan 24 12:48 llama.cpp\n",
            "drwxr-xr-x  2 root root 4096 Jan 24 12:38 merge\n",
            "drwxr-xr-x  1 root root 4096 Jan 19 14:20 sample_data\n",
            "total 14146392\n",
            "drwxr-xr-x 2 root root       4096 Jan 24 12:38 .\n",
            "drwxr-xr-x 1 root root       4096 Jan 24 13:09 ..\n",
            "-rw-r--r-- 1 root root        651 Jan 24 12:38 config.json\n",
            "-rw-r--r-- 1 root root        398 Jan 24 12:38 mergekit_config.yml\n",
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            "-rw-r--r-- 1 root root       1054 Jan 24 12:38 README.md\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Llama.cpp install\n",
        "! git clone https://github.com/ggerganov/llama.cpp\n",
        "! cd llama.cpp && make\n",
        "! python3 -m pip install -r llama.cpp/requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JNm-76uQfM7G",
        "outputId": "47e8b574-b708-4e46-e700-335b2044a0a9"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'llama.cpp'...\n",
            "remote: Enumerating objects: 16800, done.\u001b[K\n",
            "remote: Counting objects: 100% (6191/6191), done.\u001b[K\n",
            "remote: Compressing objects: 100% (348/348), done.\u001b[K\n",
            "remote: Total 16800 (delta 6039), reused 5882 (delta 5843), pack-reused 10609\u001b[K\n",
            "Receiving objects: 100% (16800/16800), 19.04 MiB | 19.60 MiB/s, done.\n",
            "Resolving deltas: 100% (11729/11729), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# converting to Llama.cpp format\n",
        "!python3 llama.cpp/convert.py merge"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vyvhnl1gnvWo",
        "outputId": "bb4bd801-6c2c-4051-f330-4976d5763bd1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model file merge/model-00001-of-00008.safetensors\n",
            "Loading model file merge/model-00001-of-00008.safetensors\n",
            "Loading model file merge/model-00002-of-00008.safetensors\n",
            "Loading model file merge/model-00003-of-00008.safetensors\n",
            "Loading model file merge/model-00004-of-00008.safetensors\n",
            "Loading model file merge/model-00005-of-00008.safetensors\n",
            "Loading model file merge/model-00006-of-00008.safetensors\n",
            "Loading model file merge/model-00007-of-00008.safetensors\n",
            "Loading model file merge/model-00008-of-00008.safetensors\n",
            "params = Params(n_vocab=32000, n_embd=4096, n_layer=32, n_ctx=32768, n_ff=14336, n_head=32, n_head_kv=8, n_experts=None, n_experts_used=None, f_norm_eps=1e-05, rope_scaling_type=None, f_rope_freq_base=10000.0, f_rope_scale=None, n_orig_ctx=None, rope_finetuned=None, ftype=None, path_model=PosixPath('merge'))\n",
            "Found vocab files: {'tokenizer.model': PosixPath('merge/tokenizer.model'), 'vocab.json': None, 'tokenizer.json': PosixPath('merge/tokenizer.json')}\n",
            "Loading vocab file 'merge/tokenizer.model', type 'spm'\n",
            "Vocab info: <SentencePieceVocab with 32000 base tokens and 0 added tokens>\n",
            "Special vocab info: <SpecialVocab with 0 merges, special tokens {'bos': 1, 'eos': 2, 'unk': 0}, add special tokens unset>\n",
            "Permuting layer 0\n",
            "Permuting layer 1\n",
            "Permuting layer 2\n",
            "Permuting layer 3\n",
            "Permuting layer 4\n",
            "Permuting layer 5\n",
            "Permuting layer 6\n",
            "Permuting layer 7\n",
            "Permuting layer 8\n",
            "Permuting layer 9\n",
            "Permuting layer 10\n",
            "Permuting layer 11\n",
            "Permuting layer 12\n",
            "Permuting layer 13\n",
            "Permuting layer 14\n",
            "Permuting layer 15\n",
            "Permuting layer 16\n",
            "Permuting layer 17\n",
            "Permuting layer 18\n",
            "Permuting layer 19\n",
            "Permuting layer 20\n",
            "Permuting layer 21\n",
            "Permuting layer 22\n",
            "Permuting layer 23\n",
            "Permuting layer 24\n",
            "Permuting layer 25\n",
            "Permuting layer 26\n",
            "Permuting layer 27\n",
            "Permuting layer 28\n",
            "Permuting layer 29\n",
            "Permuting layer 30\n",
            "Permuting layer 31\n",
            "model.layers.17.post_attention_layernorm.weight  -> blk.17.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.17.self_attn.k_proj.weight          -> blk.17.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.17.self_attn.o_proj.weight          -> blk.17.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.17.self_attn.q_proj.weight          -> blk.17.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.17.self_attn.v_proj.weight          -> blk.17.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.18.input_layernorm.weight           -> blk.18.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.18.mlp.down_proj.weight             -> blk.18.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.18.mlp.gate_proj.weight             -> blk.18.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.18.mlp.up_proj.weight               -> blk.18.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.18.post_attention_layernorm.weight  -> blk.18.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.18.self_attn.k_proj.weight          -> blk.18.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.18.self_attn.o_proj.weight          -> blk.18.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.18.self_attn.q_proj.weight          -> blk.18.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.18.self_attn.v_proj.weight          -> blk.18.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.19.input_layernorm.weight           -> blk.19.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.19.mlp.down_proj.weight             -> blk.19.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.19.mlp.gate_proj.weight             -> blk.19.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.19.mlp.up_proj.weight               -> blk.19.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.19.post_attention_layernorm.weight  -> blk.19.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.19.self_attn.k_proj.weight          -> blk.19.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.19.self_attn.o_proj.weight          -> blk.19.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.19.self_attn.q_proj.weight          -> blk.19.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.19.self_attn.v_proj.weight          -> blk.19.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.20.input_layernorm.weight           -> blk.20.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.20.mlp.down_proj.weight             -> blk.20.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.20.mlp.gate_proj.weight             -> blk.20.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.20.mlp.up_proj.weight               -> blk.20.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.20.post_attention_layernorm.weight  -> blk.20.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.20.self_attn.k_proj.weight          -> blk.20.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.20.self_attn.o_proj.weight          -> blk.20.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.20.self_attn.q_proj.weight          -> blk.20.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.20.self_attn.v_proj.weight          -> blk.20.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.21.input_layernorm.weight           -> blk.21.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.21.mlp.down_proj.weight             -> blk.21.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.21.mlp.gate_proj.weight             -> blk.21.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.21.mlp.up_proj.weight               -> blk.21.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.21.post_attention_layernorm.weight  -> blk.21.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.21.self_attn.k_proj.weight          -> blk.21.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.21.self_attn.o_proj.weight          -> blk.21.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.21.self_attn.q_proj.weight          -> blk.21.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.21.self_attn.v_proj.weight          -> blk.21.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.22.self_attn.k_proj.weight          -> blk.22.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.22.self_attn.o_proj.weight          -> blk.22.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.22.self_attn.q_proj.weight          -> blk.22.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.22.self_attn.v_proj.weight          -> blk.22.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.13.mlp.down_proj.weight             -> blk.13.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.13.mlp.gate_proj.weight             -> blk.13.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.13.post_attention_layernorm.weight  -> blk.13.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.13.self_attn.k_proj.weight          -> blk.13.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.13.self_attn.o_proj.weight          -> blk.13.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.13.self_attn.q_proj.weight          -> blk.13.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.13.self_attn.v_proj.weight          -> blk.13.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.14.input_layernorm.weight           -> blk.14.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.14.mlp.down_proj.weight             -> blk.14.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.14.mlp.gate_proj.weight             -> blk.14.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.14.mlp.up_proj.weight               -> blk.14.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.14.post_attention_layernorm.weight  -> blk.14.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.14.self_attn.k_proj.weight          -> blk.14.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.14.self_attn.o_proj.weight          -> blk.14.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.14.self_attn.q_proj.weight          -> blk.14.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.14.self_attn.v_proj.weight          -> blk.14.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.15.input_layernorm.weight           -> blk.15.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.15.mlp.down_proj.weight             -> blk.15.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.15.mlp.gate_proj.weight             -> blk.15.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.15.mlp.up_proj.weight               -> blk.15.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.15.post_attention_layernorm.weight  -> blk.15.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.15.self_attn.k_proj.weight          -> blk.15.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.15.self_attn.o_proj.weight          -> blk.15.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.15.self_attn.q_proj.weight          -> blk.15.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.15.self_attn.v_proj.weight          -> blk.15.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.16.input_layernorm.weight           -> blk.16.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.16.mlp.down_proj.weight             -> blk.16.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.16.mlp.gate_proj.weight             -> blk.16.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.16.mlp.up_proj.weight               -> blk.16.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.16.post_attention_layernorm.weight  -> blk.16.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.16.self_attn.k_proj.weight          -> blk.16.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.16.self_attn.o_proj.weight          -> blk.16.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.16.self_attn.q_proj.weight          -> blk.16.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.16.self_attn.v_proj.weight          -> blk.16.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.17.input_layernorm.weight           -> blk.17.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.17.mlp.down_proj.weight             -> blk.17.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.17.mlp.gate_proj.weight             -> blk.17.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.17.mlp.up_proj.weight               -> blk.17.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.10.input_layernorm.weight           -> blk.10.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.10.mlp.down_proj.weight             -> blk.10.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.10.mlp.gate_proj.weight             -> blk.10.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.10.mlp.up_proj.weight               -> blk.10.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.10.post_attention_layernorm.weight  -> blk.10.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.10.self_attn.k_proj.weight          -> blk.10.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.10.self_attn.o_proj.weight          -> blk.10.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.10.self_attn.q_proj.weight          -> blk.10.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.10.self_attn.v_proj.weight          -> blk.10.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.11.input_layernorm.weight           -> blk.11.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.11.mlp.down_proj.weight             -> blk.11.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.11.mlp.gate_proj.weight             -> blk.11.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.11.mlp.up_proj.weight               -> blk.11.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.11.post_attention_layernorm.weight  -> blk.11.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.11.self_attn.k_proj.weight          -> blk.11.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.11.self_attn.o_proj.weight          -> blk.11.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.11.self_attn.q_proj.weight          -> blk.11.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.11.self_attn.v_proj.weight          -> blk.11.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.12.input_layernorm.weight           -> blk.12.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.12.mlp.down_proj.weight             -> blk.12.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.12.mlp.gate_proj.weight             -> blk.12.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.12.mlp.up_proj.weight               -> blk.12.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.12.post_attention_layernorm.weight  -> blk.12.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.12.self_attn.k_proj.weight          -> blk.12.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.12.self_attn.o_proj.weight          -> blk.12.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.12.self_attn.q_proj.weight          -> blk.12.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.12.self_attn.v_proj.weight          -> blk.12.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.13.input_layernorm.weight           -> blk.13.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.13.mlp.up_proj.weight               -> blk.13.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.8.post_attention_layernorm.weight   -> blk.8.ffn_norm.weight                    | BF16   | [4096]\n",
            "model.layers.8.self_attn.k_proj.weight           -> blk.8.attn_k.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.8.self_attn.o_proj.weight           -> blk.8.attn_output.weight                 | BF16   | [4096, 4096]\n",
            "model.layers.8.self_attn.q_proj.weight           -> blk.8.attn_q.weight                      | BF16   | [4096, 4096]\n",
            "model.layers.8.self_attn.v_proj.weight           -> blk.8.attn_v.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.9.input_layernorm.weight            -> blk.9.attn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.9.mlp.down_proj.weight              -> blk.9.ffn_down.weight                    | BF16   | [4096, 14336]\n",
            "model.layers.9.mlp.gate_proj.weight              -> blk.9.ffn_gate.weight                    | BF16   | [14336, 4096]\n",
            "model.layers.9.mlp.up_proj.weight                -> blk.9.ffn_up.weight                      | BF16   | [14336, 4096]\n",
            "model.layers.9.post_attention_layernorm.weight   -> blk.9.ffn_norm.weight                    | BF16   | [4096]\n",
            "model.layers.9.self_attn.k_proj.weight           -> blk.9.attn_k.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.9.self_attn.o_proj.weight           -> blk.9.attn_output.weight                 | BF16   | [4096, 4096]\n",
            "model.layers.9.self_attn.q_proj.weight           -> blk.9.attn_q.weight                      | BF16   | [4096, 4096]\n",
            "model.layers.9.self_attn.v_proj.weight           -> blk.9.attn_v.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.4.mlp.down_proj.weight              -> blk.4.ffn_down.weight                    | BF16   | [4096, 14336]\n",
            "model.layers.4.mlp.gate_proj.weight              -> blk.4.ffn_gate.weight                    | BF16   | [14336, 4096]\n",
            "model.layers.4.post_attention_layernorm.weight   -> blk.4.ffn_norm.weight                    | BF16   | [4096]\n",
            "model.layers.4.self_attn.k_proj.weight           -> blk.4.attn_k.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.4.self_attn.o_proj.weight           -> blk.4.attn_output.weight                 | BF16   | [4096, 4096]\n",
            "model.layers.4.self_attn.q_proj.weight           -> blk.4.attn_q.weight                      | BF16   | [4096, 4096]\n",
            "model.layers.4.self_attn.v_proj.weight           -> blk.4.attn_v.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.5.input_layernorm.weight            -> blk.5.attn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.5.mlp.down_proj.weight              -> blk.5.ffn_down.weight                    | BF16   | [4096, 14336]\n",
            "model.layers.5.mlp.gate_proj.weight              -> blk.5.ffn_gate.weight                    | BF16   | [14336, 4096]\n",
            "model.layers.5.mlp.up_proj.weight                -> blk.5.ffn_up.weight                      | BF16   | [14336, 4096]\n",
            "model.layers.5.post_attention_layernorm.weight   -> blk.5.ffn_norm.weight                    | BF16   | [4096]\n",
            "model.layers.5.self_attn.k_proj.weight           -> blk.5.attn_k.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.5.self_attn.o_proj.weight           -> blk.5.attn_output.weight                 | BF16   | [4096, 4096]\n",
            "model.layers.5.self_attn.q_proj.weight           -> blk.5.attn_q.weight                      | BF16   | [4096, 4096]\n",
            "model.layers.5.self_attn.v_proj.weight           -> blk.5.attn_v.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.6.input_layernorm.weight            -> blk.6.attn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.6.mlp.down_proj.weight              -> blk.6.ffn_down.weight                    | BF16   | [4096, 14336]\n",
            "model.layers.6.mlp.gate_proj.weight              -> blk.6.ffn_gate.weight                    | BF16   | [14336, 4096]\n",
            "model.layers.6.mlp.up_proj.weight                -> blk.6.ffn_up.weight                      | BF16   | [14336, 4096]\n",
            "model.layers.6.post_attention_layernorm.weight   -> blk.6.ffn_norm.weight                    | BF16   | [4096]\n",
            "model.layers.6.self_attn.k_proj.weight           -> blk.6.attn_k.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.6.self_attn.o_proj.weight           -> blk.6.attn_output.weight                 | BF16   | [4096, 4096]\n",
            "model.layers.6.self_attn.q_proj.weight           -> blk.6.attn_q.weight                      | BF16   | [4096, 4096]\n",
            "model.layers.6.self_attn.v_proj.weight           -> blk.6.attn_v.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.7.input_layernorm.weight            -> blk.7.attn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.7.mlp.down_proj.weight              -> blk.7.ffn_down.weight                    | BF16   | [4096, 14336]\n",
            "model.layers.7.mlp.gate_proj.weight              -> blk.7.ffn_gate.weight                    | BF16   | [14336, 4096]\n",
            "model.layers.7.mlp.up_proj.weight                -> blk.7.ffn_up.weight                      | BF16   | [14336, 4096]\n",
            "model.layers.7.post_attention_layernorm.weight   -> blk.7.ffn_norm.weight                    | BF16   | [4096]\n",
            "model.layers.7.self_attn.k_proj.weight           -> blk.7.attn_k.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.7.self_attn.o_proj.weight           -> blk.7.attn_output.weight                 | BF16   | [4096, 4096]\n",
            "model.layers.7.self_attn.q_proj.weight           -> blk.7.attn_q.weight                      | BF16   | [4096, 4096]\n",
            "model.layers.7.self_attn.v_proj.weight           -> blk.7.attn_v.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.8.input_layernorm.weight            -> blk.8.attn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.8.mlp.down_proj.weight              -> blk.8.ffn_down.weight                    | BF16   | [4096, 14336]\n",
            "model.layers.8.mlp.gate_proj.weight              -> blk.8.ffn_gate.weight                    | BF16   | [14336, 4096]\n",
            "model.layers.8.mlp.up_proj.weight                -> blk.8.ffn_up.weight                      | BF16   | [14336, 4096]\n",
            "model.layers.0.input_layernorm.weight            -> blk.0.attn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.0.mlp.down_proj.weight              -> blk.0.ffn_down.weight                    | BF16   | [4096, 14336]\n",
            "model.layers.0.mlp.gate_proj.weight              -> blk.0.ffn_gate.weight                    | BF16   | [14336, 4096]\n",
            "model.layers.0.mlp.up_proj.weight                -> blk.0.ffn_up.weight                      | BF16   | [14336, 4096]\n",
            "model.layers.0.post_attention_layernorm.weight   -> blk.0.ffn_norm.weight                    | BF16   | [4096]\n",
            "model.layers.0.self_attn.k_proj.weight           -> blk.0.attn_k.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.0.self_attn.o_proj.weight           -> blk.0.attn_output.weight                 | BF16   | [4096, 4096]\n",
            "model.layers.0.self_attn.q_proj.weight           -> blk.0.attn_q.weight                      | BF16   | [4096, 4096]\n",
            "model.layers.0.self_attn.v_proj.weight           -> blk.0.attn_v.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.1.input_layernorm.weight            -> blk.1.attn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.1.mlp.down_proj.weight              -> blk.1.ffn_down.weight                    | BF16   | [4096, 14336]\n",
            "model.layers.1.mlp.gate_proj.weight              -> blk.1.ffn_gate.weight                    | BF16   | [14336, 4096]\n",
            "model.layers.1.mlp.up_proj.weight                -> blk.1.ffn_up.weight                      | BF16   | [14336, 4096]\n",
            "model.layers.1.post_attention_layernorm.weight   -> blk.1.ffn_norm.weight                    | BF16   | [4096]\n",
            "model.layers.1.self_attn.k_proj.weight           -> blk.1.attn_k.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.1.self_attn.o_proj.weight           -> blk.1.attn_output.weight                 | BF16   | [4096, 4096]\n",
            "model.layers.1.self_attn.q_proj.weight           -> blk.1.attn_q.weight                      | BF16   | [4096, 4096]\n",
            "model.layers.1.self_attn.v_proj.weight           -> blk.1.attn_v.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.2.input_layernorm.weight            -> blk.2.attn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.2.mlp.down_proj.weight              -> blk.2.ffn_down.weight                    | BF16   | [4096, 14336]\n",
            "model.layers.2.mlp.gate_proj.weight              -> blk.2.ffn_gate.weight                    | BF16   | [14336, 4096]\n",
            "model.layers.2.mlp.up_proj.weight                -> blk.2.ffn_up.weight                      | BF16   | [14336, 4096]\n",
            "model.layers.2.post_attention_layernorm.weight   -> blk.2.ffn_norm.weight                    | BF16   | [4096]\n",
            "model.layers.2.self_attn.k_proj.weight           -> blk.2.attn_k.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.2.self_attn.o_proj.weight           -> blk.2.attn_output.weight                 | BF16   | [4096, 4096]\n",
            "model.layers.2.self_attn.q_proj.weight           -> blk.2.attn_q.weight                      | BF16   | [4096, 4096]\n",
            "model.layers.2.self_attn.v_proj.weight           -> blk.2.attn_v.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.3.input_layernorm.weight            -> blk.3.attn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.3.mlp.down_proj.weight              -> blk.3.ffn_down.weight                    | BF16   | [4096, 14336]\n",
            "model.layers.3.mlp.gate_proj.weight              -> blk.3.ffn_gate.weight                    | BF16   | [14336, 4096]\n",
            "model.layers.3.mlp.up_proj.weight                -> blk.3.ffn_up.weight                      | BF16   | [14336, 4096]\n",
            "model.layers.3.post_attention_layernorm.weight   -> blk.3.ffn_norm.weight                    | BF16   | [4096]\n",
            "model.layers.3.self_attn.k_proj.weight           -> blk.3.attn_k.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.3.self_attn.o_proj.weight           -> blk.3.attn_output.weight                 | BF16   | [4096, 4096]\n",
            "model.layers.3.self_attn.q_proj.weight           -> blk.3.attn_q.weight                      | BF16   | [4096, 4096]\n",
            "model.layers.3.self_attn.v_proj.weight           -> blk.3.attn_v.weight                      | BF16   | [1024, 4096]\n",
            "model.layers.4.input_layernorm.weight            -> blk.4.attn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.4.mlp.up_proj.weight                -> blk.4.ffn_up.weight                      | BF16   | [14336, 4096]\n",
            "lm_head.weight                                   -> output.weight                            | BF16   | [32000, 4096]\n",
            "model.embed_tokens.weight                        -> token_embd.weight                        | BF16   | [32000, 4096]\n",
            "model.layers.28.post_attention_layernorm.weight  -> blk.28.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.28.self_attn.k_proj.weight          -> blk.28.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.28.self_attn.o_proj.weight          -> blk.28.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.28.self_attn.q_proj.weight          -> blk.28.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.28.self_attn.v_proj.weight          -> blk.28.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.29.input_layernorm.weight           -> blk.29.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.29.mlp.down_proj.weight             -> blk.29.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.29.mlp.gate_proj.weight             -> blk.29.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.29.mlp.up_proj.weight               -> blk.29.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.29.post_attention_layernorm.weight  -> blk.29.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.29.self_attn.k_proj.weight          -> blk.29.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.29.self_attn.o_proj.weight          -> blk.29.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.29.self_attn.q_proj.weight          -> blk.29.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.29.self_attn.v_proj.weight          -> blk.29.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.30.input_layernorm.weight           -> blk.30.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.30.mlp.down_proj.weight             -> blk.30.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.30.mlp.gate_proj.weight             -> blk.30.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.30.mlp.up_proj.weight               -> blk.30.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.30.post_attention_layernorm.weight  -> blk.30.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.30.self_attn.k_proj.weight          -> blk.30.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.30.self_attn.o_proj.weight          -> blk.30.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.30.self_attn.q_proj.weight          -> blk.30.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.30.self_attn.v_proj.weight          -> blk.30.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.31.input_layernorm.weight           -> blk.31.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.31.mlp.down_proj.weight             -> blk.31.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.31.mlp.gate_proj.weight             -> blk.31.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.31.mlp.up_proj.weight               -> blk.31.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.31.post_attention_layernorm.weight  -> blk.31.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.31.self_attn.k_proj.weight          -> blk.31.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.31.self_attn.o_proj.weight          -> blk.31.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.31.self_attn.q_proj.weight          -> blk.31.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.31.self_attn.v_proj.weight          -> blk.31.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.norm.weight                                -> output_norm.weight                       | BF16   | [4096]\n",
            "model.layers.24.mlp.down_proj.weight             -> blk.24.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.24.mlp.gate_proj.weight             -> blk.24.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.24.post_attention_layernorm.weight  -> blk.24.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.24.self_attn.k_proj.weight          -> blk.24.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.24.self_attn.o_proj.weight          -> blk.24.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.24.self_attn.q_proj.weight          -> blk.24.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.24.self_attn.v_proj.weight          -> blk.24.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.25.input_layernorm.weight           -> blk.25.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.25.mlp.down_proj.weight             -> blk.25.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.25.mlp.gate_proj.weight             -> blk.25.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.25.mlp.up_proj.weight               -> blk.25.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.25.post_attention_layernorm.weight  -> blk.25.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.25.self_attn.k_proj.weight          -> blk.25.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.25.self_attn.o_proj.weight          -> blk.25.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.25.self_attn.q_proj.weight          -> blk.25.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.25.self_attn.v_proj.weight          -> blk.25.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.26.input_layernorm.weight           -> blk.26.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.26.mlp.down_proj.weight             -> blk.26.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.26.mlp.gate_proj.weight             -> blk.26.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.26.mlp.up_proj.weight               -> blk.26.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.26.post_attention_layernorm.weight  -> blk.26.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.26.self_attn.k_proj.weight          -> blk.26.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.26.self_attn.o_proj.weight          -> blk.26.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.26.self_attn.q_proj.weight          -> blk.26.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.26.self_attn.v_proj.weight          -> blk.26.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.27.input_layernorm.weight           -> blk.27.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.27.mlp.down_proj.weight             -> blk.27.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.27.mlp.gate_proj.weight             -> blk.27.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.27.mlp.up_proj.weight               -> blk.27.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.27.post_attention_layernorm.weight  -> blk.27.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.27.self_attn.k_proj.weight          -> blk.27.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.27.self_attn.o_proj.weight          -> blk.27.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.27.self_attn.q_proj.weight          -> blk.27.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.27.self_attn.v_proj.weight          -> blk.27.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.28.input_layernorm.weight           -> blk.28.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.28.mlp.down_proj.weight             -> blk.28.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.28.mlp.gate_proj.weight             -> blk.28.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.28.mlp.up_proj.weight               -> blk.28.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.22.input_layernorm.weight           -> blk.22.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.22.mlp.down_proj.weight             -> blk.22.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.22.mlp.gate_proj.weight             -> blk.22.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.22.mlp.up_proj.weight               -> blk.22.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.22.post_attention_layernorm.weight  -> blk.22.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.23.input_layernorm.weight           -> blk.23.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.23.mlp.down_proj.weight             -> blk.23.ffn_down.weight                   | BF16   | [4096, 14336]\n",
            "model.layers.23.mlp.gate_proj.weight             -> blk.23.ffn_gate.weight                   | BF16   | [14336, 4096]\n",
            "model.layers.23.mlp.up_proj.weight               -> blk.23.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "model.layers.23.post_attention_layernorm.weight  -> blk.23.ffn_norm.weight                   | BF16   | [4096]\n",
            "model.layers.23.self_attn.k_proj.weight          -> blk.23.attn_k.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.23.self_attn.o_proj.weight          -> blk.23.attn_output.weight                | BF16   | [4096, 4096]\n",
            "model.layers.23.self_attn.q_proj.weight          -> blk.23.attn_q.weight                     | BF16   | [4096, 4096]\n",
            "model.layers.23.self_attn.v_proj.weight          -> blk.23.attn_v.weight                     | BF16   | [1024, 4096]\n",
            "model.layers.24.input_layernorm.weight           -> blk.24.attn_norm.weight                  | BF16   | [4096]\n",
            "model.layers.24.mlp.up_proj.weight               -> blk.24.ffn_up.weight                     | BF16   | [14336, 4096]\n",
            "Writing merge/ggml-model-f16.gguf, format 1\n",
            "Ignoring added_tokens.json since model matches vocab size without it.\n",
            "gguf: This GGUF file is for Little Endian only\n",
            "gguf: Setting special token type bos to 1\n",
            "gguf: Setting special token type eos to 2\n",
            "gguf: Setting special token type unk to 0\n",
            "[  1/291] Writing tensor blk.17.ffn_norm.weight                 | size   4096           | type F32  | T+   0\n",
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            "[291/291] Writing tensor blk.24.ffn_up.weight                   | size  14336 x   4096  | type F16  | T+ 194\n",
            "Wrote merge/ggml-model-f16.gguf\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# quantizing to q5_k_m GGUF with Llama.cpp\n",
        "! llama.cpp/quantize merge/ggml-model-f16.gguf merge/m_q5_k_m.gguf q5_k_m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "upQDhBdDo5mT",
        "outputId": "7752ca4d-5d8c-490f-ca93-b7734cc699da"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "main: build = 1963 (c9b316c7)\n",
            "main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu\n",
            "main: quantizing 'merge/ggml-model-f16.gguf' to 'merge/m_q5_k_m.gguf' as Q5_K_M\n",
            "llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from merge/ggml-model-f16.gguf (version GGUF V3 (latest))\n",
            "llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n",
            "llama_model_loader: - kv   0:                       general.architecture str              = llama\n",
            "llama_model_loader: - kv   1:                               general.name str              = .\n",
            "llama_model_loader: - kv   2:                       llama.context_length u32              = 32768\n",
            "llama_model_loader: - kv   3:                     llama.embedding_length u32              = 4096\n",
            "llama_model_loader: - kv   4:                          llama.block_count u32              = 32\n",
            "llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 14336\n",
            "llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 128\n",
            "llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32\n",
            "llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 8\n",
            "llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010\n",
            "llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000\n",
            "llama_model_loader: - kv  11:                          general.file_type u32              = 1\n",
            "llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama\n",
            "llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = [\"<unk>\", \"<s>\", \"</s>\", \"<0x00>\", \"<...\n",
            "llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...\n",
            "llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...\n",
            "llama_model_loader: - kv  16:                tokenizer.ggml.bos_token_id u32              = 1\n",
            "llama_model_loader: - kv  17:                tokenizer.ggml.eos_token_id u32              = 2\n",
            "llama_model_loader: - kv  18:            tokenizer.ggml.unknown_token_id u32              = 0\n",
            "llama_model_loader: - type  f32:   65 tensors\n",
            "llama_model_loader: - type  f16:  226 tensors\n",
            "llama_model_quantize_internal: meta size = 734848 bytes\n",
            "[   1/ 291]               blk.17.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[   2/ 291]                 blk.17.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[   3/ 291]            blk.17.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[   4/ 291]                 blk.17.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[   5/ 291]                 blk.17.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[   6/ 291]              blk.18.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[   7/ 291]               blk.18.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[   8/ 291]               blk.18.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[   9/ 291]                 blk.18.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  10/ 291]               blk.18.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  11/ 291]                 blk.18.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  12/ 291]            blk.18.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  13/ 291]                 blk.18.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  14/ 291]                 blk.18.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  15/ 291]              blk.19.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  16/ 291]               blk.19.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  17/ 291]               blk.19.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  18/ 291]                 blk.19.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  19/ 291]               blk.19.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  20/ 291]                 blk.19.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  21/ 291]            blk.19.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  22/ 291]                 blk.19.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  23/ 291]                 blk.19.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  24/ 291]              blk.20.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  25/ 291]               blk.20.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  26/ 291]               blk.20.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  27/ 291]                 blk.20.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  28/ 291]               blk.20.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  29/ 291]                 blk.20.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  30/ 291]            blk.20.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  31/ 291]                 blk.20.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  32/ 291]                 blk.20.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  33/ 291]              blk.21.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  34/ 291]               blk.21.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  35/ 291]               blk.21.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  36/ 291]                 blk.21.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  37/ 291]               blk.21.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  38/ 291]                 blk.21.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  39/ 291]            blk.21.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
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            "[  42/ 291]                 blk.22.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  43/ 291]            blk.22.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  44/ 291]                 blk.22.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  45/ 291]                 blk.22.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  46/ 291]               blk.13.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  47/ 291]               blk.13.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  48/ 291]               blk.13.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  49/ 291]                 blk.13.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  50/ 291]            blk.13.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  51/ 291]                 blk.13.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  52/ 291]                 blk.13.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  53/ 291]              blk.14.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  54/ 291]               blk.14.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  55/ 291]               blk.14.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  56/ 291]                 blk.14.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  57/ 291]               blk.14.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  58/ 291]                 blk.14.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  59/ 291]            blk.14.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  60/ 291]                 blk.14.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  61/ 291]                 blk.14.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  62/ 291]              blk.15.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  63/ 291]               blk.15.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  64/ 291]               blk.15.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  65/ 291]                 blk.15.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  66/ 291]               blk.15.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  67/ 291]                 blk.15.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  68/ 291]            blk.15.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  69/ 291]                 blk.15.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  70/ 291]                 blk.15.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  71/ 291]              blk.16.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  72/ 291]               blk.16.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  73/ 291]               blk.16.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  74/ 291]                 blk.16.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  75/ 291]               blk.16.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  76/ 291]                 blk.16.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  77/ 291]            blk.16.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  78/ 291]                 blk.16.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  79/ 291]                 blk.16.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  80/ 291]              blk.17.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  81/ 291]               blk.17.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  82/ 291]               blk.17.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  83/ 291]                 blk.17.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  84/ 291]              blk.10.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  85/ 291]               blk.10.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  86/ 291]               blk.10.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  87/ 291]                 blk.10.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  88/ 291]               blk.10.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  89/ 291]                 blk.10.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  90/ 291]            blk.10.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  91/ 291]                 blk.10.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  92/ 291]                 blk.10.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  93/ 291]              blk.11.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  94/ 291]               blk.11.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  95/ 291]               blk.11.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  96/ 291]                 blk.11.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  97/ 291]               blk.11.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  98/ 291]                 blk.11.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  99/ 291]            blk.11.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 100/ 291]                 blk.11.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 101/ 291]                 blk.11.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 102/ 291]              blk.12.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 103/ 291]               blk.12.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 104/ 291]               blk.12.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 105/ 291]                 blk.12.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 106/ 291]               blk.12.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 107/ 291]                 blk.12.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 108/ 291]            blk.12.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 109/ 291]                 blk.12.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 110/ 291]                 blk.12.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 111/ 291]              blk.13.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 112/ 291]                 blk.13.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 113/ 291]                blk.8.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 114/ 291]                  blk.8.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 115/ 291]             blk.8.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 116/ 291]                  blk.8.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 117/ 291]                  blk.8.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 118/ 291]               blk.9.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 119/ 291]                blk.9.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 120/ 291]                blk.9.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 121/ 291]                  blk.9.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 122/ 291]                blk.9.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 123/ 291]                  blk.9.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 124/ 291]             blk.9.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 125/ 291]                  blk.9.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 126/ 291]                  blk.9.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 127/ 291]                blk.4.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 128/ 291]                blk.4.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 129/ 291]                blk.4.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 130/ 291]                  blk.4.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 131/ 291]             blk.4.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 132/ 291]                  blk.4.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 133/ 291]                  blk.4.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 134/ 291]               blk.5.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 135/ 291]                blk.5.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 136/ 291]                blk.5.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 137/ 291]                  blk.5.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 138/ 291]                blk.5.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 139/ 291]                  blk.5.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 140/ 291]             blk.5.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 141/ 291]                  blk.5.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 142/ 291]                  blk.5.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 143/ 291]               blk.6.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 144/ 291]                blk.6.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 145/ 291]                blk.6.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 146/ 291]                  blk.6.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 147/ 291]                blk.6.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 148/ 291]                  blk.6.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 149/ 291]             blk.6.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 150/ 291]                  blk.6.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 151/ 291]                  blk.6.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 152/ 291]               blk.7.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 153/ 291]                blk.7.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 154/ 291]                blk.7.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 155/ 291]                  blk.7.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 156/ 291]                blk.7.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 157/ 291]                  blk.7.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 158/ 291]             blk.7.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 159/ 291]                  blk.7.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 160/ 291]                  blk.7.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 161/ 291]               blk.8.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 162/ 291]                blk.8.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 163/ 291]                blk.8.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 164/ 291]                  blk.8.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 165/ 291]               blk.0.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 166/ 291]                blk.0.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 167/ 291]                blk.0.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 168/ 291]                  blk.0.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 169/ 291]                blk.0.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 170/ 291]                  blk.0.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 171/ 291]             blk.0.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 172/ 291]                  blk.0.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 173/ 291]                  blk.0.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 174/ 291]               blk.1.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 175/ 291]                blk.1.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 176/ 291]                blk.1.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 177/ 291]                  blk.1.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 178/ 291]                blk.1.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 179/ 291]                  blk.1.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 180/ 291]             blk.1.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 181/ 291]                  blk.1.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 182/ 291]                  blk.1.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 183/ 291]               blk.2.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 184/ 291]                blk.2.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 185/ 291]                blk.2.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 186/ 291]                  blk.2.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 187/ 291]                blk.2.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 188/ 291]                  blk.2.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 189/ 291]             blk.2.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 190/ 291]                  blk.2.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 191/ 291]                  blk.2.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 192/ 291]               blk.3.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 193/ 291]                blk.3.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, quantizing to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
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            "[ 278/ 291]               blk.22.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 279/ 291]                 blk.22.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 280/ 291]               blk.22.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 281/ 291]              blk.23.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
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            "[ 283/ 291]               blk.23.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 284/ 291]                 blk.23.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 285/ 291]               blk.23.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 286/ 291]                 blk.23.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 287/ 291]            blk.23.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 288/ 291]                 blk.23.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, quantizing to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 289/ 291]                 blk.23.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, quantizing to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 290/ 291]              blk.24.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 291/ 291]                 blk.24.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, quantizing to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "llama_model_quantize_internal: model size  = 13813.02 MB\n",
            "llama_model_quantize_internal: quant size  =  4892.99 MB\n",
            "\n",
            "main: quantize time = 918268.61 ms\n",
            "main:    total time = 918268.61 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! ls -la merge |grep gguf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5R_9gsbquUdT",
        "outputId": "39f8cde9-bb41-4504-86cb-c8221bcc1659"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-rw-r--r-- 1 root root 14484731456 Jan 24 13:22 ggml-model-f16.gguf\n",
            "-rw-r--r-- 1 root root  5131409024 Jan 24 13:42 m_q5_k_m.gguf\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# https://stackoverflow.com/questions/48774285/how-to-download-file-created-in-colaboratory-workspace\n",
        "from google.colab import files\n",
        "files.download('merge/m_q5_k_m.gguf')"
      ],
      "metadata": {
        "id": "SzT6CR96u-z8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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 = 'mlabonne' # @param {type:\"string\"}\n",
        "token = 'HF_TOKEN' # @param {type:\"string\"}\n",
        "license = \"apache-2.0\" # @param [\"apache-2.0\", \"cc-by-nc-4.0\", \"mit\", \"openrail\"] {allow-input: true}\n",
        "\n",
        "!pip install -qU huggingface_hub\n",
        "\n",
        "import yaml\n",
        "\n",
        "from huggingface_hub import ModelCard, ModelCardData, HfApi\n",
        "from google.colab import userdata\n",
        "from jinja2 import Template\n",
        "\n",
        "if branch == \"main\":\n",
        "    template_text = \"\"\"\n",
        "---\n",
        "license: {{ license }}\n",
        "base_model:\n",
        "{%- for model in models %}\n",
        "  - {{ model }}\n",
        "{%- endfor %}\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 [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):\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",
        "## 💻 Usage\n",
        "\n",
        "```python\n",
        "!pip install -qU transformers accelerate\n",
        "\n",
        "from transformers import AutoTokenizer\n",
        "import transformers\n",
        "import torch\n",
        "\n",
        "model = \"{{ username }}/{{ model_name }}\"\n",
        "messages = [{\"role\": \"user\", \"content\": \"What is a large language model?\"}]\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model)\n",
        "prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
        "pipeline = transformers.pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "\n",
        "outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)\n",
        "print(outputs[0][\"generated_text\"])\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",
        "elif branch == \"mixtral\":\n",
        "    template_text = \"\"\"\n",
        "---\n",
        "license: {{ license }}\n",
        "base_model:\n",
        "{%- for model in models %}\n",
        "  - {{ model }}\n",
        "{%- endfor %}\n",
        "tags:\n",
        "- moe\n",
        "- frankenmoe\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 Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):\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",
        "## 💻 Usage\n",
        "\n",
        "```python\n",
        "!pip install -qU transformers bitsandbytes accelerate\n",
        "\n",
        "from transformers import AutoTokenizer\n",
        "import transformers\n",
        "import torch\n",
        "\n",
        "model = \"{{ username }}/{{ model_name }}\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model)\n",
        "pipeline = transformers.pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    model_kwargs={\"torch_dtype\": torch.float16, \"load_in_4bit\": True},\n",
        ")\n",
        "\n",
        "messages = [{\"role\": \"user\", \"content\": \"Explain what a Mixture of Experts is in less than 100 words.\"}]\n",
        "prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
        "outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)\n",
        "print(outputs[0][\"generated_text\"])\n",
        "```\n",
        "\"\"\"\n",
        "\n",
        "    # Create a Jinja template object\n",
        "    jinja_template = Template(template_text.strip())\n",
        "\n",
        "    # Fill the template\n",
        "    data = yaml.safe_load(yaml_config)\n",
        "    models = [model['source_model'] for model in data['experts']]\n",
        "\n",
        "    content = jinja_template.render(\n",
        "        model_name=MODEL_NAME,\n",
        "        models=models,\n",
        "        yaml_config=yaml_config,\n",
        "        username=username,\n",
        "        license=license\n",
        "    )\n",
        "\n",
        "# Save the model card\n",
        "card = ModelCard(content)\n",
        "card.save('merge/README.md')\n",
        "\n",
        "# Defined in the secrets tab in Google Colab\n",
        "api = HfApi(token=userdata.get(token))\n",
        "\n",
        "# Upload merge folder\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=\"merge\",\n",
        ")"
      ],
      "metadata": {
        "id": "ik0V0dF55gfU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}