diff --git "a/HINDI_GEMMA_9B_A60_all_evals.ipynb" "b/HINDI_GEMMA_9B_A60_all_evals.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "JeByetuw4Z8p",
+ "outputId": "40f3df96-0ef2-4c83-fc7b-869868ec4084"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.11/dist-packages (0.27.1)\n",
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+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (3.10)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (2.3.0)\n",
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+ ]
+ }
+ ],
+ "source": [
+ "!pip install huggingface_hub pandas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 17,
+ "referenced_widgets": [
+ "e8ce03452a074d9eb6728e087b368ed2",
+ "35a027d34b45414ab1e0fa02e3c963ef",
+ "51eda975a89e40368e9044b035c78ea4",
+ "8806ac65c1224312a0a86e795ee68c48",
+ "9eee0d24c4be474c8e49f9d5430f6e5f",
+ "a572131b41c7411480c2d077b74bf4d6",
+ "8f386f5b3b5a4b2fa03cc8c8b8160140",
+ "2bb9207c214c4124a4137158206face6",
+ "f025709bcf164d3b9f72a17211a4cb76",
+ "0622e8c7a97b4f63b2583792dd5c6714",
+ "a21ad5d9130e4760b3915ebc9aa35e19",
+ "53bf7541ee9044c3bcb85ca5011d071b",
+ "f66914590b174c2e92ed57f4e4bc383d",
+ "ac0f255d5f4f49339125a8d5d2b89ad6",
+ "358d0a2e351e480885f208925aa88ba7",
+ "7de0479f56c343498e1f1c1441932462",
+ "45e4ef83c5e94aed9da14b534f3d2e0c",
+ "0d8748823faf49d2be43a206532b56e4",
+ "cc4b3c4870b547d68ce204ba2fd73522",
+ "416def283eb841da8e4fb66cb175942c"
+ ]
+ },
+ "id": "-KBQcoiuz-0C",
+ "outputId": "42e0f573-5d8c-486a-ac22-87fb7bdebe5f"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "VBox(children=(HTML(value='
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m319.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hInstalling collected packages: xxhash, shtab, protobuf, hf_transfer, fsspec, dill, multiprocess, tyro, xformers, datasets, cut_cross_entropy, bitsandbytes, trl, unsloth_zoo, unsloth\n",
+ " Attempting uninstall: protobuf\n",
+ " Found existing installation: protobuf 4.25.5\n",
+ " Uninstalling protobuf-4.25.5:\n",
+ " Successfully uninstalled protobuf-4.25.5\n",
+ " Attempting uninstall: fsspec\n",
+ " Found existing installation: fsspec 2024.10.0\n",
+ " Uninstalling fsspec-2024.10.0:\n",
+ " Successfully uninstalled fsspec-2024.10.0\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",
+ "grpcio-status 1.62.3 requires protobuf>=4.21.6, but you have protobuf 3.20.3 which is incompatible.\n",
+ "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n",
+ "tensorflow-metadata 1.16.1 requires protobuf<6.0.0dev,>=4.25.2; python_version >= \"3.11\", but you have protobuf 3.20.3 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0mSuccessfully installed bitsandbytes-0.45.1 cut_cross_entropy-25.1.1 datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 hf_transfer-0.1.9 multiprocess-0.70.16 protobuf-3.20.3 shtab-1.7.1 trl-0.13.0 tyro-0.9.13 unsloth-2025.1.6 unsloth_zoo-2025.1.5 xformers-0.0.29.post1 xxhash-3.5.0\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.colab-display-data+json": {
+ "pip_warning": {
+ "packages": [
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+ },
+ "id": "6628030adbf74535b477723769edccfa"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (24.2)\n",
+ "Collecting ninja\n",
+ " Downloading ninja-1.11.1.3-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (5.3 kB)\n",
+ "Requirement already satisfied: einops in /usr/local/lib/python3.11/dist-packages (0.8.0)\n",
+ "Collecting flash-attn>=2.6.3\n",
+ " Downloading flash_attn-2.7.3.tar.gz (3.2 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m97.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ "Using cached ninja-1.11.1.3-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (422 kB)\n",
+ "Building wheels for collected packages: flash-attn\n",
+ " Building wheel for flash-attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for flash-attn: filename=flash_attn-2.7.3-cp311-cp311-linux_x86_64.whl size=191363917 sha256=b1243e9b86687348a5ab03a073abacdf8e3d5e9e4b7e5326a183f47348c5dfba\n",
+ " Stored in directory: /root/.cache/pip/wheels/d0/a3/f9/48d2706cb2eac05ec0dc144bf6954fe47bb3c2cd0de280765e\n",
+ "Successfully built flash-attn\n",
+ "Installing collected packages: ninja, flash-attn\n",
+ "Successfully installed flash-attn-2.7.3 ninja-1.11.1.3\n"
+ ]
+ }
+ ],
+ "source": [
+ "! pip install unsloth --upgrade --no-cache-dir\n",
+ "import torch\n",
+ "if torch.cuda.get_device_capability()[0] >= 8:\n",
+ " !pip install --no-deps packaging ninja einops \"flash-attn>=2.6.3\""
+ ]
+ },
+ {
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+ },
+ "id": "YJ4YoJzs-3r3",
+ "outputId": "f3e1d359-42cf-406e-8926-558949bf4ba8"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "0it [00:00, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
+ "==((====))== Unsloth 2025.1.6: Fast Gemma2 patching. Transformers: 4.47.1.\n",
+ " \\\\ /| GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform: Linux.\n",
+ "O^O/ \\_/ \\ Torch: 2.5.1+cu121. CUDA: 8.0. CUDA Toolkit: 12.1. Triton: 3.1.0\n",
+ "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. FA2 = True]\n",
+ " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n",
+ "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+ ]
+ },
+ {
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+ "version_major": 2,
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+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ },
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+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
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+ ],
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "special_tokens_map.json: 0%| | 0.00/636 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "e503a7683038427d961a285a72889466"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "tokenizer.json: 0%| | 0.00/17.5M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "43580cd30501473f81c75466da073a64"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "from unsloth import FastLanguageModel\n",
+ "import torch\n",
+ "max_seq_length = 2048\n",
+ "dtype = None\n",
+ "load_in_4bit = False\n",
+ "model, tokenizer = FastLanguageModel.from_pretrained(model_name = \"google/gemma-2-9b-it\", max_seq_length = max_seq_length, dtype = dtype, load_in_4bit = load_in_4bit,)\n",
+ "\n",
+ "################## MODELS THAT CAN BE USED : ~2-3B RANGE ###########################\n",
+ "# google/gemma-2-2b-it\n",
+ "# meta-llama/Llama-3.2-3B-Instruct\n",
+ "# Qwen/Qwen2.5-3B-Instruct\n",
+ "# microsoft/Phi-3.5-mini-instruct\n",
+ "################## MODELS THAT CAN BE USED : ~7-9B RANGE ###########################\n",
+ "# meta-llama/Llama-3.1-8B-Instruct\n",
+ "# google/gemma-2-9b-it\n",
+ "# Qwen/Qwen2.5-7B-Instruct\n",
+ "# mistralai/Mistral-7B-Instruct-v0.3\n",
+ "################## MODELS THAT CAN BE USED : ~12-14B RANGE ###########################\n",
+ "# Qwen/Qwen2.5-14B-Instruct\n",
+ "# mistralai/Mistral-Nemo-Instruct-2407\n",
+ "# microsoft/phi-4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "id": "rIhaGb6-_H1G",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "21024a66-d970-407f-a3c9-481b68f50c85"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Unsloth 2025.1.6 patched 42 layers with 42 QKV layers, 42 O layers and 42 MLP layers.\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = FastLanguageModel.get_peft_model(model, r = 16, target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
+ " lora_alpha = 16, lora_dropout = 0, bias = \"none\", use_gradient_checkpointing = \"unsloth\", random_state = 1024, use_rslora = False, loftq_config = None,)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "id": "oZEOF6vfEn2T",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 423
+ },
+ "outputId": "ea5f7886-30f6-4898-e45b-83b1ebae45b6"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " Input \\\n",
+ "0 Write a python function to find the index of s... \n",
+ "1 Post mortem staining gets fixed after - ### A)... \n",
+ "2 In our daily life, many of us feel stressed mo... \n",
+ "3 the average of thirteen numbers is 9 . the ave... \n",
+ "4 They took the cure at the spring rather than t... \n",
+ "... ... \n",
+ "33767 यदि $x = X$ और $y = 15$ है, तो $(x - y)(x + y)... \n",
+ "33768 इस संशोधन के राष्ट्रीय राजधानी क्षेत्र के भविष... \n",
+ "33769 FFather was a hardworking man who delivered br... \n",
+ "33770 कोलकाता के लिए 5-दिन की यात्रा योजना:\\n\\n**दिन... \n",
+ "33771 सभी मान खोजें $x > 4$ जो संतुष्ट करते हैं\\n\\[\\... \n",
+ "\n",
+ " Output LANG \\\n",
+ "0 import math \\r\\ndef find_Index(n): \\r\\n x =... en \n",
+ "1 D en \n",
+ "2 A en \n",
+ "3 To find the middle number, we can first calcul... en \n",
+ "4 B en \n",
+ "... ... ... \n",
+ "33767 10 hi \n",
+ "33768 इस संशोधन के राष्ट्रीय राजधानी क्षेत्र के भविष... hi \n",
+ "33769 B en \n",
+ "33770 दिन 1: विक्टोरिया मेमोरियल, सेंट पॉल का कैथेड्... hi \n",
+ "33771 दी गई समीकरण से,\\n\\[\\sqrt{x + 4 \\sqrt{x - 4}} ... hi \n",
+ "\n",
+ " type subject_name topic; subject \n",
+ "0 NaN NaN NaN NaN \n",
+ "1 NaN Forensic Medicine NaN NaN \n",
+ "2 NaN NaN NaN NaN \n",
+ "3 NaN NaN NaN NaN \n",
+ "4 NaN NaN NaN NaN \n",
+ "... ... ... ... ... \n",
+ "33767 NaN NaN NaN NaN \n",
+ "33768 NaN NaN NaN NaN \n",
+ "33769 NaN NaN NaN NaN \n",
+ "33770 NaN NaN NaN NaN \n",
+ "33771 Intermediate Algebra NaN NaN NaN \n",
+ "\n",
+ "[33772 rows x 7 columns]"
+ ],
+ "text/html": [
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+ " Write a python function to find the index of s... | \n",
+ " import math \\r\\ndef find_Index(n): \\r\\n x =... | \n",
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+ " NaN | \n",
+ " NaN | \n",
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+ " NaN | \n",
+ " NaN | \n",
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+ " 3 | \n",
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+ " To find the middle number, we can first calcul... | \n",
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\n",
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+ " 33767 | \n",
+ " यदि $x = X$ और $y = 15$ है, तो $(x - y)(x + y)... | \n",
+ " 10 | \n",
+ " hi | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
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\n",
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+ " 33768 | \n",
+ " इस संशोधन के राष्ट्रीय राजधानी क्षेत्र के भविष... | \n",
+ " इस संशोधन के राष्ट्रीय राजधानी क्षेत्र के भविष... | \n",
+ " hi | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 33769 | \n",
+ " FFather was a hardworking man who delivered br... | \n",
+ " B | \n",
+ " en | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 33770 | \n",
+ " कोलकाता के लिए 5-दिन की यात्रा योजना:\\n\\n**दिन... | \n",
+ " दिन 1: विक्टोरिया मेमोरियल, सेंट पॉल का कैथेड्... | \n",
+ " hi | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " 33771 | \n",
+ " सभी मान खोजें $x > 4$ जो संतुष्ट करते हैं\\n\\[\\... | \n",
+ " दी गई समीकरण से,\\n\\[\\sqrt{x + 4 \\sqrt{x - 4}} ... | \n",
+ " hi | \n",
+ " Intermediate Algebra | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ "
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+ "
33772 rows × 7 columns
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+ "
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+ "
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+ "
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+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "dataframe",
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+ "summary": "{\n \"name\": \"df\",\n \"rows\": 33772,\n \"fields\": [\n {\n \"column\": \"Input\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 32243,\n \"samples\": [\n \"\\u092c\\u0947\\u0938\\u0928 \\u0914\\u0930 \\u092e\\u093f\\u0930\\u094d\\u091a \\u0915\\u0940 \\u0938\\u092c\\u094d\\u095b\\u0940 \\u0930\\u0947\\u0938\\u093f\\u092a\\u0940 - Besan Aur Mirch Ki Sabzi (Recipe In Hindi) ### LONG ANSWER ### \",\n \"\\u090f\\u0915 \\u092a\\u093e\\u092f\\u0925\\u0928 \\u092b\\u093c\\u0902\\u0915\\u094d\\u0936\\u0928 \\u0932\\u093f\\u0916\\u0947\\u0902 \\u091c\\u094b \\u0926\\u093f\\u090f \\u0917\\u090f \\u0938\\u0902\\u0916\\u094d\\u092f\\u093e\\u0913\\u0902 \\u0915\\u0940 \\u0938\\u0942\\u091a\\u0940 \\u092e\\u0947\\u0902 \\u092a\\u0939\\u0932\\u093e \\u0935\\u093f\\u0937\\u092e \\u0938\\u0902\\u0916\\u094d\\u092f\\u093e \\u0916\\u094b\\u091c\\u0947\\u0964 ### CODE ### \",\n \"How does the chemical composition of crude oil relate to its impact on different ecosystems during an oil spill event? ### LONG ANSWER ### \"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Output\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 23383,\n \"samples\": [\n \"The case records of a Disciplinary case are returned to the Ministries/Departments/State Governments along with the Advice of the Commission. Therefore, the Commission cannot provide copies of the case records pertaining to any Disciplinary case.\",\n \"To find the magnetic field strength at a distance of 5 cm from the wire, we can use the formula for the magnetic field around a straight current-carrying conductor, which is given by Ampere's Law:\\n\\nB = (\\u03bc\\u2080 * I) / (2 * \\u03c0 * r)\\n\\nwhere B is the magnetic field strength, \\u03bc\\u2080 is the permeability of free space (4\\u03c0 \\u00d7 10\\u207b\\u2077 Tm/A), I is the current flowing through the wire, and r is the distance from the wire.\\n\\nGiven the current I = 3 A and the distance r = 5 cm = 0.05 m, we can plug these values into the formula:\\n\\nB = (4\\u03c0 \\u00d7 10\\u207b\\u2077 Tm/A * 3 A) / (2 * \\u03c0 * 0.05 m)\\n\\nThe \\u03c0 in the numerator and denominator cancels out:\\n\\nB = (4 \\u00d7 10\\u207b\\u2077 Tm/A * 3 A) / (2 * 0.05 m)\\n\\nNow, we can simplify and calculate the magnetic field strength:\\n\\nB = (12 \\u00d7 10\\u207b\\u2077 T) / (0.1 m)\\nB = 1.2 \\u00d7 10\\u207b\\u2076 T\\n\\nSo, the magnetic field strength at a distance of 5 cm from the wire is 1.2 \\u00d7 10\\u207b\\u2076 T (tesla).\",\n \"\\u0926\\u0902\\u0921 \\u0915\\u0940 \\u092e\\u0941\\u0926\\u094d\\u0930\\u093e \\u0909\\u0938 \\u0924\\u093e\\u0930\\u0940\\u0916 \\u0938\\u0947 \\u0932\\u0940 \\u091c\\u093e\\u0924\\u0940 \\u0939\\u0948 \\u091c\\u093f\\u0938 \\u092a\\u0930 \\u0907\\u0938\\u0947 \\u0932\\u093e\\u0917\\u0942 \\u0915\\u093f\\u092f\\u093e \\u091c\\u093e\\u0924\\u093e \\u0939\\u0948 \\u0938\\u0947 \\u0932\\u0947\\u0915\\u0930 \\u0909\\u0938 \\u0924\\u093e\\u0930\\u0940\\u0916 \\u0924\\u0915 \\u091c\\u092c \\u092f\\u0939 \\u092a\\u094d\\u0930\\u092d\\u093e\\u0935\\u0940 \\u0930\\u0939\\u0928\\u093e \\u092c\\u0902\\u0926 \\u0915\\u0930 \\u0926\\u0947\\u0924\\u0940 \\u0939\\u0948\\u0964\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"LANG\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"hi\",\n \"en\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"Prealgebra\",\n \"Number Theory\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"subject_name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 21,\n \"samples\": [\n \"Forensic Medicine\",\n \"Unknown\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"topic;\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 97,\n \"samples\": [\n \"Paleontology\",\n \"Botany\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"subject\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 57,\n \"samples\": [\n \"us_foreign_policy\",\n \"high_school_world_history\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
+ }
+ },
+ "metadata": {},
+ "execution_count": 19
+ }
+ ],
+ "source": [
+ "df = df_train.copy()\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "id": "zYNCBXnxEvGg"
+ },
+ "outputs": [],
+ "source": [
+ "from datasets import Dataset\n",
+ "df_train['Output'] = df_train['Output'].astype(str)\n",
+ "dataset = Dataset.from_pandas(df_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "id": "orRdPSsSE7u2"
+ },
+ "outputs": [],
+ "source": [
+ "alpaca_prompt = \"\"\"### INPUT: {} RESPONSE: {}\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "id": "3fax1umiFFG6",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "4662ac782a304cacbef110988c714072",
+ "3b94f3cfdf1a4874af0855e01cda71b4",
+ "0ce867b907654a33a78bdf4c844df58c",
+ "039b331234c043159b825b0bd40fd46d",
+ "96102f55b6024d68a3be4cd1defbf680",
+ "3318b894ed864678aced9f1699ab304a",
+ "838dd43599504bad82bb094ac78d6d9d",
+ "16729a4fb609486c95f0a34986fba615",
+ "2afd5057fd97405fb2b67bf47e3c30b1",
+ "4a4381b98212426bb15ca3bb55d8a295",
+ "21f8f4cdd33d4729b854d77772e55c13"
+ ]
+ },
+ "outputId": "d50aa8a4-262c-4641-943d-384c890b70c1"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/33772 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "4662ac782a304cacbef110988c714072"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "EOS_TOKEN = tokenizer.eos_token\n",
+ "def formatting_prompts_func(examples):\n",
+ " inputs = examples[\"Input\"]\n",
+ " outputs = examples[\"Output\"]\n",
+ " texts = []\n",
+ " for input_text, output_text in zip(inputs, outputs):\n",
+ " text = alpaca_prompt.format(input_text, output_text) + EOS_TOKEN\n",
+ " texts.append(text)\n",
+ " return {\"text\": texts}\n",
+ "dataset = dataset.map(formatting_prompts_func, batched=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "id": "jR_ZltTgFFEe",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "b4899c55e2614ea6a3f920b98927c556",
+ "e5ed3dd0be43442da4bade2bfee42529",
+ "ec582549270e4fa9aa3f9abcba722c8e",
+ "a1cccd8b4ff64c5381a20335f64849a4",
+ "0e477c8134fc42a1abe60c2be03bf775",
+ "dc09181d5e0d48dcb1c5a5db80c1d574",
+ "a6b128c5e86c48d8b55f41328704e4f6",
+ "ed5db6b0adaf40328ad43bc901664c13",
+ "60a4f30e0cad4ee4a4de7005d4a773d0",
+ "2aeb7dcbbe614ddbb9a64f3edf1ecb2f",
+ "d63b3ff8aabb49b2bff132372f1af7ee"
+ ]
+ },
+ "outputId": "95287d1c-b3c3-4510-a53f-470688a90574"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/33772 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "b4899c55e2614ea6a3f920b98927c556"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "def count_tokens(example):\n",
+ " return {\"input_token_count\": len(tokenizer.tokenize(str(example.get(\"Input\", \"\"))))}\n",
+ "dataset = dataset.map(count_tokens)\n",
+ "dataset = dataset.sort(\"input_token_count\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "id": "zzKKRBXdFFCB",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "9cbd85a78aa24adb9cee41f2e61700c9",
+ "2107d18a84f94a7ea5d0de7415aad6bd",
+ "a6538e95c80b4128af2badfa79376f04",
+ "79702507817142acad2f979f95fd0844",
+ "a3a9c122cbf744df8a96b74fce642850",
+ "2a4be370076e4ccd81c2c8ad13ed22b7",
+ "18eda7880ff948c0bc8306aab4e18436",
+ "e94d3cc3e5914599ba4ec578cddb9b51",
+ "f5ee7fbde23a4a1397676744c398d77c",
+ "e3ea23527c9d4c78a3cb115c0c19133e",
+ "37864852f13c442f8a6ee4248fd8d765"
+ ]
+ },
+ "outputId": "6467ce82-5d26-4b2f-8359-e7d219a335b6"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Flattening the indices: 0%| | 0/33772 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "9cbd85a78aa24adb9cee41f2e61700c9"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "from datasets import concatenate_datasets\n",
+ "last_200 = dataset.select(range(len(dataset) - 200, len(dataset)))\n",
+ "remaining = dataset.select(range(len(dataset) - 200))\n",
+ "dataset = concatenate_datasets([last_200, remaining])\n",
+ "dataset = dataset.flatten_indices()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "id": "WK-V70834Z9B",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "0c148321-e6cd-4add-85df-a111cdbe9fcd"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.1/44.1 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.1/10.1 MB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "! pip -q uninstall transformers -y\n",
+ "! pip -q install transformers==4.47.1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "id": "0bl3owAZFE_m",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "d5e15fbcc18a42bb86d888a03a3b09cd",
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+ "8b9994f822844709ba2ed68aa207ed59"
+ ]
+ },
+ "outputId": "6a149b96-17eb-4e70-e3e6-810cb62fb248"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map (num_proc=2): 0%| | 0/33772 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d5e15fbcc18a42bb86d888a03a3b09cd"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "from trl import SFTTrainer\n",
+ "from transformers import TrainingArguments\n",
+ "from unsloth import is_bfloat16_supported\n",
+ "trainer = SFTTrainer(\n",
+ " model = model,\n",
+ " tokenizer = tokenizer,\n",
+ " train_dataset = dataset,\n",
+ " dataset_text_field = \"text\",\n",
+ " max_seq_length = max_seq_length,\n",
+ " dataset_num_proc = 2,\n",
+ " packing = False,\n",
+ " args = TrainingArguments(\n",
+ " per_device_train_batch_size = 4,\n",
+ " gradient_accumulation_steps = 4,\n",
+ " num_train_epochs = 1,\n",
+ " learning_rate = 2e-5,\n",
+ " fp16 = not is_bfloat16_supported(),\n",
+ " bf16 = is_bfloat16_supported(),\n",
+ " logging_steps = 1000,\n",
+ " optim = \"adamw_8bit\",\n",
+ " weight_decay = 0.01,\n",
+ " lr_scheduler_type = \"linear\",\n",
+ " seed = 1024,\n",
+ " output_dir = \"outputs\",\n",
+ " report_to = \"none\",\n",
+ " ),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 345
+ },
+ "id": "RcONa7bxFE7J",
+ "outputId": "4724bd43-f27f-4d3d-a915-430c568ab3be"
+ },
+ "outputs": [
+ {
+ "metadata": {
+ "tags": null
+ },
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
+ " \\\\ /| Num examples = 33,772 | Num Epochs = 1\n",
+ "O^O/ \\_/ \\ Batch size per device = 4 | Gradient Accumulation steps = 4\n",
+ "\\ / Total batch size = 16 | Total steps = 2,110\n",
+ " \"-____-\" Number of trainable parameters = 54,018,048\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
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+ "source": [
+ "trainer_stats = trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xj6XdJWNF6Pb"
+ },
+ "source": [
+ "#**MODEL SAVING**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ULl7losCFj9L"
+ },
+ "outputs": [],
+ "source": [
+ "!wait"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "cEGk9Cm1FmoH"
+ },
+ "outputs": [],
+ "source": [
+ "if True: model.push_to_hub_merged(\"DrishtiSharma/HINDI-GEMMA-9B-A60\", tokenizer, save_method = \"merged_16bit\")\n",
+ "# USE YOU HF ACCOUNT AND NOT THE ORG\n",
+ "# NAME FORMAT : HINDI-MODELNAME-EXTENSION-A/B-00 - USE A/B is BENCHMARK DATASETS WERE SET TO TRUE/FALSE IN FLAGS , THE NUMBER IS THE RATIO USED ABOVE"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "U5gGbbSNF1oB"
+ },
+ "outputs": [],
+ "source": [
+ "# SET THE REPO PRIVATE AFTER UPLOAD\n",
+ "# UPLOAD THE CSV TO THE REPO AFTER IT IS MADE PRIVATE\n",
+ "# CSV >> THE CSV SAVED IN YOUR RUNTIME"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.colab import runtime\n",
+ "runtime.unassign()"
+ ],
+ "metadata": {
+ "id": "brN9DtmHhYPS"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "3BcP1kfkhcFb"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ],
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