Inference.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Hrazhan/kurdish-llama/blob/main/Inference.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RcXtD7J6Hr1P",
        "outputId": "de263199-d9cc-4a52-be7f-a72b7bf4e093"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'kurdish-llama'...\n",
            "remote: Enumerating objects: 68, done.\u001b[K\n",
            "remote: Counting objects: 100% (68/68), done.\u001b[K\n",
            "remote: Compressing objects: 100% (48/48), done.\u001b[K\n",
            "remote: Total 68 (delta 32), reused 51 (delta 18), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (68/68), 13.91 MiB | 2.01 MiB/s, done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/Hrazhan/kurdish-llama"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd kurdish-llama"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZK1VelLH37F",
        "outputId": "3d522a7f-dd75-43a9-a31b-72a9d3b1f371"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/kurdish-llama\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jMh95TtQIArO",
        "outputId": "e27e8535-3d0d-49da-8264-f330e2cccbb9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-s2elrhys\n",
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            "Building wheels for collected packages: transformers, ffmpy, pathtools\n",
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            "Installing collected packages: tokenizers, sentencepiece, safetensors, pydub, pathtools, ffmpy, bitsandbytes, xxhash, websockets, uc-micro-py, smmap, setproctitle, sentry-sdk, semantic-version, python-multipart, orjson, multidict, h11, frozenlist, docker-pycreds, dill, async-timeout, aiofiles, yarl, uvicorn, starlette, responses, multiprocess, mdit-py-plugins, linkify-it-py, huggingface-hub, httpcore, gitdb, aiosignal, transformers, httpx, GitPython, fastapi, aiohttp, wandb, gradio-client, gradio, datasets, accelerate, peft\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import PeftModel\n",
        "from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig\n",
        "\n",
        "tokenizer = LlamaTokenizer.from_pretrained(\"decapoda-research/llama-7b-hf\")\n",
        "\n",
        "model = LlamaForCausalLM.from_pretrained(\n",
        "    \"decapoda-research/llama-7b-hf\",\n",
        "    load_in_8bit=True,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "model = PeftModel.from_pretrained(model, \"razhan/kurdish-llama-lora\")\n",
        "\n",
        "PROMPT = \"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
        "### Instruction:\n",
        "شتێکم پێ بڵێ دەربارەی کورد\n",
        "### Response:\"\"\"\n",
        "\n",
        "inputs = tokenizer(\n",
        "    PROMPT,\n",
        "    return_tensors=\"pt\",\n",
        ")\n",
        "input_ids = inputs[\"input_ids\"].cuda()\n",
        "\n",
        "generation_config = GenerationConfig(\n",
        "    temperature=0.6,\n",
        "    top_p=0.95,\n",
        "    repetition_penalty=1.15,\n",
        ")\n",
        "print(\"Generating...\")\n",
        "generation_output = model.generate(\n",
        "    input_ids=input_ids,\n",
        "    generation_config=generation_config,\n",
        "    return_dict_in_generate=True,\n",
        "    output_scores=True,\n",
        "    max_new_tokens=128,\n",
        ")\n",
        "for s in generation_output.sequences:\n",
        "    print(tokenizer.decode(s))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "id": "PG-9Hh_lIEi9",
        "outputId": "e883dd6b-6d52-431b-c28d-ff7ea16dc8b6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('8013'), PosixPath('//172.28.0.1'), PosixPath('http')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-s-2mk5kacnppjtu --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n"
          ]
        },
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          "text": [
            "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
            "The tokenizer class you load from this checkpoint is 'LLaMATokenizer'. \n",
            "The class this function is called from is 'LlamaTokenizer'.\n"
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            ],
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            "Generating...\n",
            " ⁇  Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
            "### Instruction:\n",
            "شتێکم پێ بڵێ دەربارەی کورد\n",
            "### Response:\n",
            "ئەنجامی سەرچاوەی خۆی لەگەڵ هەفتەیەکی زیاتر لە ٢٠ ساڵ بە شێوەیەکی بەرفراوان بە شێوەیەکی بەرفراوان بەدەست بهێنێت، بەڵام\n"
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      "cell_type": "code",
      "source": [
        "PROMPT ='''Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction:\n",
        "کۆدی Python بنووسە بۆ ناسینەوەی ژمارەی پرایم\n",
        "### Response:\n",
        "'''"
      ],
      "metadata": {
        "id": "7I4ojGzkIpej"
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      "execution_count": 8,
      "outputs": []
    },
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        "inputs = tokenizer(\n",
        "    PROMPT,\n",
        "    return_tensors=\"pt\",\n",
        ")\n",
        "input_ids = inputs[\"input_ids\"].cuda()\n",
        "\n",
        "generation_config = GenerationConfig(\n",
        "    temperature=0.6,\n",
        "    top_p=0.95,\n",
        "    repetition_penalty=1.15,\n",
        ")\n",
        "print(\"Generating...\")\n",
        "generation_output = model.generate(\n",
        "    input_ids=input_ids,\n",
        "    generation_config=generation_config,\n",
        "    return_dict_in_generate=True,\n",
        "    output_scores=True,\n",
        "    max_new_tokens=128,\n",
        ")\n",
        "for s in generation_output.sequences:\n",
        "    print(tokenizer.decode(s))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2IQMzL4bJW1z",
        "outputId": "f37964d9-d228-4a3d-fe7d-01447a9ec492"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating...\n",
            " ⁇  Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
            "\n",
            "### Instruction:\n",
            "کۆدی Python بنووسە بۆ ناسینەوەی ژمارەی پرایم\n",
            "### Response:\n",
            "لێرەدا کۆدی ئەگەری بۆ ناسینەوەی ژمارەی پرایم دەخەینەڕوو:\n",
            "```python\n",
            "def count_primes(n):\n",
            "    primes = []\n",
            "    for i in range(2, n + 1):\n",
            "        if (i % 2 == 0) or (i > n):\n",
            "            continue\n",
            "        else:\n",
            "            primes.append(i)\n",
            "\n"
          ]
        }
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