model_conversion_hf_gguf.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ugv49HlGQsgl"
      },
      "source": [
        "**1: Install the huggingface_hub Package **\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iRA8pfUBFLdu",
        "outputId": "1c304402-a9e2-493e-dd25-277e58e4e529"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.23.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.15.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.4)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2024.6.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install huggingface_hub"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2JKFJ0Z6Q3wp"
      },
      "source": [
        "**2. Download a Model Snapshot from Hugging Face Hub**\n"
      ]
    },
    {
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        },
        "id": "Q2XpHP47FSme",
        "outputId": "d156d24e-fb14-4dae-d7c7-6ec2e3207a35"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1194: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.\n",
            "For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
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              "model_id": "f3f4cc47ca794620898e5cd6968f3ff2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Fetching 12 files:   0%|          | 0/12 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "09a682ec206c47c4b5f799e66ae807c0",
              "version_major": 2,
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            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e13c1cf9ec9f4b658becb0fd79e483f8",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "feb620d415e94472891c9c296578d549",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9251a78b917f4336908ce570e15a7898",
              "version_major": 2,
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d9da1873e51e4effb10d0e38f7edad49",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "89ad6048a2b640baa07fcfef0f34a96d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/7.06k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "83e1389669e64d5e861090cd069bfe47",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model-00002-of-00003.safetensors:   0%|          | 0.00/4.98G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "694e4e4f68bf4425a8b69bd164984345",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3c8eecab663b4e388de5870cea5f31a9",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4c13c4e1d7984277b40a1c2d2c529a1c",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "133561aa052e4163a837bf21717698fc",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e58bced155de4d558d6ff7d5d3012df3",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/Indic-gemma-2b-finetuned-sft-Navarasa'"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from huggingface_hub import snapshot_download\n",
        "model_id=\"Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa-2.0\"\n",
        "snapshot_download(repo_id=model_id, local_dir=\"Indic-gemma-2b-finetuned-sft-Navarasa\",\n",
        "                  local_dir_use_symlinks=False, revision=\"main\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KHUhoIpXFkY7",
        "outputId": "448446b1-3d64-457b-e8a0-8f638aa806b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total 9.4G\n",
            "4.0K drwxr-xr-x 3 root root 4.0K Jul  3 10:38 .\n",
            "4.0K drwxr-xr-x 1 root root 4.0K Jul  3 10:38 ..\n",
            "4.0K -rw-r--r-- 1 root root  659 Jul  3 10:37 config.json\n",
            "4.0K -rw-r--r-- 1 root root  132 Jul  3 10:37 generation_config.json\n",
            "4.0K -rw-r--r-- 1 root root 1.6K Jul  3 10:37 .gitattributes\n",
            "4.0K drwxr-xr-x 3 root root 4.0K Jul  3 10:37 .huggingface\n",
            "4.6G -rw-r--r-- 1 root root 4.6G Jul  3 10:38 model-00001-of-00003.safetensors\n",
            "4.7G -rw-r--r-- 1 root root 4.7G Jul  3 10:38 model-00002-of-00003.safetensors\n",
            "129M -rw-r--r-- 1 root root 129M Jul  3 10:37 model-00003-of-00003.safetensors\n",
            " 16K -rw-r--r-- 1 root root  14K Jul  3 10:37 model.safetensors.index.json\n",
            "8.0K -rw-r--r-- 1 root root 6.9K Jul  3 10:37 README.md\n",
            "4.0K -rw-r--r-- 1 root root  555 Jul  3 10:37 special_tokens_map.json\n",
            "4.0K -rw-r--r-- 1 root root 1.1K Jul  3 10:37 tokenizer_config.json\n",
            " 17M -rw-r--r-- 1 root root  17M Jul  3 10:37 tokenizer.json\n",
            "4.1M -rw-r--r-- 1 root root 4.1M Jul  3 10:37 tokenizer.model\n"
          ]
        }
      ],
      "source": [
        "!ls -lash Indic-gemma-2b-finetuned-sft-Navarasa"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n1CqrEhtQ-HI"
      },
      "source": [
        "**3. Clone the llama.cpp Repository**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RKRJwyZ9F8L2",
        "outputId": "f5b2ec0d-9586-4466-e881-d4fa8ec2b395"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'llama.cpp'...\n",
            "remote: Enumerating objects: 28460, done.\u001b[K\n",
            "remote: Counting objects: 100% (5056/5056), done.\u001b[K\n",
            "remote: Compressing objects: 100% (279/279), done.\u001b[K\n",
            "remote: Total 28460 (delta 4893), reused 4810 (delta 4777), pack-reused 23404\u001b[K\n",
            "Receiving objects: 100% (28460/28460), 52.36 MiB | 19.42 MiB/s, done.\n",
            "Resolving deltas: 100% (20369/20369), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/ggerganov/llama.cpp.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9L0p1gbYGChg",
        "outputId": "fa5c7521-a12e-4251-ebb6-a02ecfa82691"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting numpy~=1.26.4 (from -r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 1))\n",
            "  Downloading numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n",
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            "\u001b[?25hCollecting sentencepiece~=0.2.0 (from -r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 2))\n",
            "  Downloading sentencepiece-0.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hRequirement already satisfied: transformers<5.0.0,>=4.40.1 in /usr/local/lib/python3.10/dist-packages (from -r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 3)) (4.41.2)\n",
            "Collecting gguf>=0.1.0 (from -r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 4))\n",
            "  Downloading gguf-0.6.0-py3-none-any.whl (23 kB)\n",
            "Collecting protobuf<5.0.0,>=4.21.0 (from -r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 5))\n",
            "  Downloading protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl (294 kB)\n",
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            "\u001b[?25hCollecting torch~=2.2.1 (from -r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2))\n",
            "  Downloading torch-2.2.2-cp310-cp310-manylinux1_x86_64.whl (755.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m755.5/755.5 MB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.40.1->-r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 3)) (3.15.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.40.1->-r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 3)) (0.23.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.40.1->-r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 3)) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.40.1->-r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 3)) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.40.1->-r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 3)) (2024.5.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.40.1->-r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 3)) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.40.1->-r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 3)) (0.19.1)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.40.1->-r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 3)) (0.4.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.40.1->-r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 3)) (4.66.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2)) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2)) (1.12.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2)) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2)) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2)) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2))\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2))\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2))\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2))\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2))\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2))\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2))\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2))\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2))\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2))\n",
            "  Downloading nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.0/166.0 MB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-nvtx-cu12==12.1.105 (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2))\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Collecting triton==2.2.0 (from torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2))\n",
            "  Downloading triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (167.9 MB)\n",
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            "\u001b[?25hCollecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2))\n",
            "  Downloading nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m57.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2)) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers<5.0.0,>=4.40.1->-r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 3)) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers<5.0.0,>=4.40.1->-r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 3)) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers<5.0.0,>=4.40.1->-r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 3)) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers<5.0.0,>=4.40.1->-r llama.cpp/./requirements/requirements-convert-legacy-llama.txt (line 3)) (2024.6.2)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch~=2.2.1->-r llama.cpp/./requirements/requirements-convert-hf-to-gguf.txt (line 2)) (1.3.0)\n",
            "Installing collected packages: sentencepiece, triton, protobuf, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, nvidia-cusparse-cu12, nvidia-cudnn-cu12, gguf, nvidia-cusolver-cu12, torch\n",
            "  Attempting uninstall: sentencepiece\n",
            "    Found existing installation: sentencepiece 0.1.99\n",
            "    Uninstalling sentencepiece-0.1.99:\n",
            "      Successfully uninstalled sentencepiece-0.1.99\n",
            "  Attempting uninstall: triton\n",
            "    Found existing installation: triton 2.3.0\n",
            "    Uninstalling triton-2.3.0:\n",
            "      Successfully uninstalled triton-2.3.0\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.20.3\n",
            "    Uninstalling protobuf-3.20.3:\n",
            "      Successfully uninstalled protobuf-3.20.3\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.25.2\n",
            "    Uninstalling numpy-1.25.2:\n",
            "      Successfully uninstalled numpy-1.25.2\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.3.0+cu121\n",
            "    Uninstalling torch-2.3.0+cu121:\n",
            "      Successfully uninstalled torch-2.3.0+cu121\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 4.25.3 which is incompatible.\n",
            "torchaudio 2.3.0+cu121 requires torch==2.3.0, but you have torch 2.2.2 which is incompatible.\n",
            "torchtext 0.18.0 requires torch>=2.3.0, but you have torch 2.2.2 which is incompatible.\n",
            "torchvision 0.18.0+cu121 requires torch==2.3.0, but you have torch 2.2.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed gguf-0.6.0 numpy-1.26.4 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 protobuf-4.25.3 sentencepiece-0.2.0 torch-2.2.2 triton-2.2.0\n"
          ]
        }
      ],
      "source": [
        "!pip install -r llama.cpp/requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6RwrY43MGluR",
        "outputId": "54aef76d-f775-47a8-d04c-e098e76a0b76"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "usage: convert-hf-to-gguf.py [-h] [--vocab-only] [--awq-path AWQ_PATH] [--outfile OUTFILE]\n",
            "                             [--outtype {f32,f16,bf16,q8_0,auto}] [--bigendian] [--use-temp-file]\n",
            "                             [--no-lazy] [--model-name MODEL_NAME] [--verbose]\n",
            "                             [--split-max-tensors SPLIT_MAX_TENSORS]\n",
            "                             [--split-max-size SPLIT_MAX_SIZE] [--dry-run]\n",
            "                             [--no-tensor-first-split]\n",
            "                             model\n",
            "\n",
            "Convert a huggingface model to a GGML compatible file\n",
            "\n",
            "positional arguments:\n",
            "  model                 directory containing model file\n",
            "\n",
            "options:\n",
            "  -h, --help            show this help message and exit\n",
            "  --vocab-only          extract only the vocab\n",
            "  --awq-path AWQ_PATH   Path to scale awq cache file\n",
            "  --outfile OUTFILE     path to write to; default: based on input. {ftype} will be replaced by the\n",
            "                        outtype.\n",
            "  --outtype {f32,f16,bf16,q8_0,auto}\n",
            "                        output format - use f32 for float32, f16 for float16, bf16 for bfloat16,\n",
            "                        q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending\n",
            "                        on the first loaded tensor type\n",
            "  --bigendian           model is executed on big endian machine\n",
            "  --use-temp-file       use the tempfile library while processing (helpful when running out of\n",
            "                        memory, process killed)\n",
            "  --no-lazy             use more RAM by computing all outputs before writing (use in case lazy\n",
            "                        evaluation is broken)\n",
            "  --model-name MODEL_NAME\n",
            "                        name of the model\n",
            "  --verbose             increase output verbosity\n",
            "  --split-max-tensors SPLIT_MAX_TENSORS\n",
            "                        max tensors in each split\n",
            "  --split-max-size SPLIT_MAX_SIZE\n",
            "                        max size per split N(M|G)\n",
            "  --dry-run             only print out a split plan and exit, without writing any new files\n",
            "  --no-tensor-first-split\n",
            "                        do not add tensors to the first split (disabled by default)\n"
          ]
        }
      ],
      "source": [
        "!python llama.cpp/convert-hf-to-gguf.py -h"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LPnKPowLRO-n"
      },
      "source": [
        "**4.Convert the Hugging Face Model to GGUF Format**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bRSUzovG8G1",
        "outputId": "2a63415c-f60f-498c-935e-a4f71915ff0b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:hf-to-gguf:Loading model: Indic-gemma-2b-finetuned-sft-Navarasa\n",
            "INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only\n",
            "INFO:hf-to-gguf:Set model parameters\n",
            "INFO:hf-to-gguf:Set model tokenizer\n",
            "INFO:gguf.vocab:Setting special token type bos to 2\n",
            "INFO:gguf.vocab:Setting special token type eos to 1\n",
            "INFO:gguf.vocab:Setting special token type unk to 3\n",
            "INFO:gguf.vocab:Setting special token type pad to 0\n",
            "INFO:gguf.vocab:Setting add_bos_token to True\n",
            "INFO:gguf.vocab:Setting add_eos_token to False\n",
            "INFO:gguf.vocab:Setting special token type prefix to 67\n",
            "INFO:gguf.vocab:Setting special token type suffix to 69\n",
            "INFO:gguf.vocab:Setting special token type middle to 68\n",
            "WARNING:gguf.vocab:No handler for special token type fsep with id 70 - skipping\n",
            "INFO:gguf.vocab:Setting special token type eot to 107\n",
            "INFO:hf-to-gguf:Exporting model...\n",
            "INFO:hf-to-gguf:gguf: loading model weight map from 'model.safetensors.index.json'\n",
            "INFO:hf-to-gguf:gguf: loading model part 'model-00001-of-00003.safetensors'\n",
            "INFO:hf-to-gguf:token_embd.weight,         torch.float32 --> Q8_0, shape = {2048, 256000}\n",
            "INFO:hf-to-gguf:blk.0.attn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.0.ffn_down.weight,     torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.0.ffn_gate.weight,     torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.0.ffn_up.weight,       torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.0.ffn_norm.weight,     torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.0.attn_k.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.0.attn_output.weight,  torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.0.attn_q.weight,       torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.0.attn_v.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.1.attn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.1.ffn_down.weight,     torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.1.ffn_gate.weight,     torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.1.ffn_up.weight,       torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.1.ffn_norm.weight,     torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.1.attn_k.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.1.attn_output.weight,  torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.1.attn_q.weight,       torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.1.attn_v.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.2.attn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.2.ffn_down.weight,     torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.2.ffn_gate.weight,     torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.2.ffn_up.weight,       torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.2.ffn_norm.weight,     torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.2.attn_k.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.2.attn_output.weight,  torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.2.attn_q.weight,       torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.2.attn_v.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.3.attn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.3.ffn_down.weight,     torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.3.ffn_gate.weight,     torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.3.ffn_up.weight,       torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.3.ffn_norm.weight,     torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.3.attn_k.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.3.attn_output.weight,  torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.3.attn_q.weight,       torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.3.attn_v.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.4.attn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.4.ffn_down.weight,     torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.4.ffn_gate.weight,     torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.4.ffn_up.weight,       torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.4.ffn_norm.weight,     torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.4.attn_k.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.4.attn_output.weight,  torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.4.attn_q.weight,       torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.4.attn_v.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.5.attn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.5.ffn_down.weight,     torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.5.ffn_gate.weight,     torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.5.ffn_up.weight,       torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.5.ffn_norm.weight,     torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.5.attn_k.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.5.attn_output.weight,  torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.5.attn_q.weight,       torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.5.attn_v.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.6.ffn_gate.weight,     torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.6.attn_k.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.6.attn_output.weight,  torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.6.attn_q.weight,       torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.6.attn_v.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'model-00002-of-00003.safetensors'\n",
            "INFO:hf-to-gguf:blk.10.attn_norm.weight,   torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.10.ffn_down.weight,    torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.10.ffn_gate.weight,    torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.10.ffn_up.weight,      torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.10.ffn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.10.attn_k.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.10.attn_output.weight, torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.10.attn_q.weight,      torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.10.attn_v.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.11.attn_norm.weight,   torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.11.ffn_down.weight,    torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.11.ffn_gate.weight,    torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.11.ffn_up.weight,      torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.11.ffn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.11.attn_k.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.11.attn_output.weight, torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.11.attn_q.weight,      torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.11.attn_v.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.12.attn_norm.weight,   torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.12.ffn_down.weight,    torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.12.ffn_gate.weight,    torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.12.ffn_up.weight,      torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.12.ffn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.12.attn_k.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.12.attn_output.weight, torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.12.attn_q.weight,      torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.12.attn_v.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.13.attn_norm.weight,   torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.13.ffn_down.weight,    torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.13.ffn_gate.weight,    torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.13.ffn_up.weight,      torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.13.ffn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.13.attn_k.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.13.attn_output.weight, torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.13.attn_q.weight,      torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.13.attn_v.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.14.attn_norm.weight,   torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.14.ffn_down.weight,    torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.14.ffn_gate.weight,    torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.14.ffn_up.weight,      torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.14.ffn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.14.attn_k.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.14.attn_output.weight, torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.14.attn_q.weight,      torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.14.attn_v.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.15.attn_norm.weight,   torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.15.ffn_down.weight,    torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.15.ffn_gate.weight,    torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.15.ffn_up.weight,      torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.15.ffn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.15.attn_k.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.15.attn_output.weight, torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.15.attn_q.weight,      torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.15.attn_v.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.16.attn_norm.weight,   torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.16.ffn_down.weight,    torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.16.ffn_gate.weight,    torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.16.ffn_up.weight,      torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.16.ffn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.16.attn_k.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.16.attn_output.weight, torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.16.attn_q.weight,      torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.16.attn_v.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.17.ffn_gate.weight,    torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.17.ffn_up.weight,      torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.17.attn_k.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.17.attn_output.weight, torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.17.attn_q.weight,      torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.17.attn_v.weight,      torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.6.attn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.6.ffn_down.weight,     torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.6.ffn_up.weight,       torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.6.ffn_norm.weight,     torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.7.attn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.7.ffn_down.weight,     torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.7.ffn_gate.weight,     torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.7.ffn_up.weight,       torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.7.ffn_norm.weight,     torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.7.attn_k.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.7.attn_output.weight,  torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.7.attn_q.weight,       torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.7.attn_v.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.8.attn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.8.ffn_down.weight,     torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.8.ffn_gate.weight,     torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.8.ffn_up.weight,       torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.8.ffn_norm.weight,     torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.8.attn_k.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.8.attn_output.weight,  torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.8.attn_q.weight,       torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.8.attn_v.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.9.attn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.9.ffn_down.weight,     torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.9.ffn_gate.weight,     torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.9.ffn_up.weight,       torch.float32 --> Q8_0, shape = {2048, 16384}\n",
            "INFO:hf-to-gguf:blk.9.ffn_norm.weight,     torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.9.attn_k.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:blk.9.attn_output.weight,  torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.9.attn_q.weight,       torch.float32 --> Q8_0, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.9.attn_v.weight,       torch.float32 --> Q8_0, shape = {2048, 256}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'model-00003-of-00003.safetensors'\n",
            "INFO:hf-to-gguf:blk.17.attn_norm.weight,   torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.17.ffn_down.weight,    torch.float32 --> Q8_0, shape = {16384, 2048}\n",
            "INFO:hf-to-gguf:blk.17.ffn_norm.weight,    torch.float32 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:output_norm.weight,        torch.float32 --> F32, shape = {2048}\n",
            "INFO:gguf.gguf_writer:Writing the following files:\n",
            "INFO:gguf.gguf_writer:Indic-gemma-2b-finetuned-sft-Navarasa-2.0.gguf: n_tensors = 164, total_size = 2.7G\n",
            "Writing: 100% 2.66G/2.66G [01:24<00:00, 31.4Mbyte/s]\n",
            "INFO:hf-to-gguf:Model successfully exported.\n"
          ]
        }
      ],
      "source": [
        "!python llama.cpp/convert-hf-to-gguf.py Indic-gemma-2b-finetuned-sft-Navarasa \\\n",
        "  --outfile Indic-gemma-2b-finetuned-sft-Navarasa-2.0.gguf \\\n",
        "  --outtype q8_0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pV1HX4syIRyI"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "glJxg88UQ7Jq",
        "outputId": "ab2598a8-5118-45e2-dcae-489ae62c9b98"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.5G -rw-r--r-- 1 root root 2.5G Jul  2 06:25 Indic-gemma-2b-finetuned-sft-Navarasa-2.0.gguf\n"
          ]
        }
      ],
      "source": [
        "!ls -lash Indic-gemma-2b-finetuned-sft-Navarasa-2.0.gguf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MbCt3YbBRWgZ"
      },
      "source": [
        "**5.Pushing the model a Repository on Hugging Face Hub and Upload the GGUF Model**"
      ]
    },
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              "Indic-gemma-2b-finetuned-sft-Navarasa-2.0.gguf:   0%|          | 0.00/2.67G [00:00<?, ?B/s]"
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              "CommitInfo(commit_url='https://huggingface.co/agie-ai/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.gguf/commit/08951e04e28a018e25ee710ec32b6709df396e93', commit_message='Upload Indic-gemma-2b-finetuned-sft-Navarasa-2.0.gguf with huggingface_hub', commit_description='', oid='08951e04e28a018e25ee710ec32b6709df396e93', pr_url=None, pr_revision=None, pr_num=None)"
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          "metadata": {},
          "output_type": "execute_result"
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      ],
      "source": [
        "from huggingface_hub import HfApi\n",
        "import os\n",
        "\n",
        "# Set the token as an environment variable (recommended for security)\n",
        "os.environ[\"HUGGING_FACE_HUB_TOKEN\"] = \"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\"\n",
        "\n",
        "api = HfApi()\n",
        "#change your-own-account/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.gguf\n",
        "\n",
        "model_id = \"shaadclt/Indic-gemma-2b-finetuned-sft-Navarasa-2.0.gguf\"\n",
        "api.create_repo(model_id, exist_ok=True, repo_type=\"model\") # Use the api object to create the repo\n",
        "api.upload_file(\n",
        "    path_or_fileobj=\"Indic-gemma-2b-finetuned-sft-Navarasa-2.0.gguf\",\n",
        "    path_in_repo=\"Indic-gemma-2b-finetuned-sft-Navarasa-2.0.gguf\",\n",
        "    repo_id=model_id,\n",
        ")"
      ]
    }
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