code_llama.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L74SrnlygqKm"
      },
      "source": [
        "# Code LLama\n",
        "\n",
        "- News: https://about.fb.com/news/2023/08/code-llama-ai-for-coding/\n",
        "- Repo: https://github.com/facebookresearch/codellama\n",
        "- Blog post: https://hf.co/blog/codellama\n",
        "- Models: https://hf.co/codellama\n",
        "- Code playground: https://huggingface.co/spaces/codellama/codellama-playground\n",
        "- Chat playground: https://huggingface.co/spaces/codellama/codellama-13b-chat\n",
        "- Code LLM Leaderboard: https://huggingface.co/spaces/bigcode/multilingual-code-evals\n",
        "\n",
        "Code below comes from https://huggingface.co/blog/codellama."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4UrmwZcNgYfr",
        "outputId": "2f3ec99e-19f1-4876-802a-d61fd420d324"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/huggingface/transformers.git@main\n",
            "  Cloning https://github.com/huggingface/transformers.git (to revision main) to /tmp/pip-req-build-u3i5b6ds\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-u3i5b6ds\n",
            "  Resolved https://github.com/huggingface/transformers.git to commit 960807f62e53676723ab8281019219864ef3db4d\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting accelerate\n",
            "  Downloading accelerate-0.22.0-py3-none-any.whl (251 kB)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.33.0.dev0) (2023.7.22)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Building wheels for collected packages: transformers\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers: filename=transformers-4.33.0.dev0-py3-none-any.whl size=7586983 sha256=2540a47e57269517221bffd11c310e441548af0ef965f83367bbd8913a70c10b\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-y55pz7n8/wheels/cf/59/82/6492402e887a68975030bf8c06532260abc16abb7ccd8127cc\n",
            "Successfully built transformers\n",
            "Installing collected packages: tokenizers, safetensors, bitsandbytes, huggingface-hub, transformers, accelerate\n",
            "Successfully installed accelerate-0.22.0 bitsandbytes-0.41.1 huggingface-hub-0.16.4 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.0.dev0\n"
          ]
        }
      ],
      "source": [
        "!pip install git+https://github.com/huggingface/transformers.git@main accelerate bitsandbytes"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Code Completion"
      ],
      "metadata": {
        "id": "-jER--kJOdPd"
      }
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      "outputs": [
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb9629e21e2549b990a7f63d9cfc7f59"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f90c3f7a27db4f46a9d5f54516a633c8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d1be2bb30f9f41ac9a03809355dde3ec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Result: def fibonacci(n):\n",
            "    if n == 0:\n",
            "        return 0\n",
            "    elif n == 1:\n",
            "        return 1\n",
            "    else:\n",
            "        return fibonacci(n-1) + fibonacci(n-2)\n",
            "\n",
            "\n",
            "def fibonacci_recursive(n):\n",
            "    if n == 0:\n",
            "        return 0\n",
            "    elif n == 1:\n",
            "        return 1\n",
            "    else:\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "import transformers\n",
        "import torch\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"codellama/CodeLlama-7b-hf\")\n",
        "pipeline = transformers.pipeline(\n",
        "    \"text-generation\",\n",
        "    model=\"codellama/CodeLlama-7b-hf\",\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "\n",
        "sequences = pipeline(\n",
        "    'def fibonacci(',\n",
        "    do_sample=True,\n",
        "    temperature=0.2,\n",
        "    top_p=0.9,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        "    max_length=100,\n",
        ")\n",
        "for seq in sequences:\n",
        "    print(f\"Result: {seq['generated_text']}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Code Infilling"
      ],
      "metadata": {
        "id": "zi3OtmjCOhLA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EVbWRKFehbHa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364,
          "referenced_widgets": [
            "dcbaa93d836b4cc3998bcbb912cf3528",
            "f9f046704fb54f6eb0c55badd6323577",
            "7511595b3c93414bb999a1b66321b1e2",
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            "a2e00acde83d4f679aab4933bb68828b",
            "b15eac1e789c41608a551b89d60abea7",
            "ee64122513ea4693b66fca1c86710362",
            "76c7c26efd7a4b79a060091fda031bb5"
          ]
        },
        "outputId": "47af4aad-aac0-4155-f250-417b666b15e2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dcbaa93d836b4cc3998bcbb912cf3528"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s> <PRE> def remove_non_ascii(s: str) -> str:\n",
            "    \"\"\"  <SUF>\n",
            "    return result\n",
            " <MID>Remove non-ASCII characters from a string.\n",
            "\n",
            "    Args:\n",
            "        s: The string to remove non-ASCII characters from.\n",
            "\n",
            "    Returns:\n",
            "        The string with non-ASCII characters removed.\n",
            "    \"\"\"\n",
            "    result = \"\"\n",
            "    for c in s:\n",
            "        if ord(c) < 128:\n",
            "            result += c <EOT></s>\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "import transformers\n",
        "import torch\n",
        "\n",
        "model_id = \"codellama/CodeLlama-7b-hf\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    torch_dtype=torch.float16\n",
        ").to(\"cuda\")\n",
        "\n",
        "prefix = 'def remove_non_ascii(s: str) -> str:\\n    \"\"\" '\n",
        "suffix = \"\\n    return result\\n\"\n",
        "\n",
        "prompt = f\"<PRE> {prefix} <SUF>{suffix} <MID>\"\n",
        "inputs = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "output = model.generate(\n",
        "    inputs[\"input_ids\"],\n",
        "    max_new_tokens=200,\n",
        "    do_sample=False,\n",
        ")\n",
        "output = output[0].to(\"cpu\")\n",
        "print(tokenizer.decode(output))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "import transformers\n",
        "import torch\n",
        "\n",
        "model_id = \"codellama/CodeLlama-7b-hf\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    torch_dtype=torch.float16\n",
        ").to(\"cuda\")\n",
        "\n",
        "system = \"Provide answers in JavaScript\"\n",
        "user = \"Write a function that computes the set of sums of all contiguous sublists of a given list.\"\n",
        "\n",
        "prompt = f\"<s><<SYS>>\\\\n{system}\\\\n<</SYS>>\\\\n\\\\n{user}\"\n",
        "inputs = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=False).to(\"cuda\")\n",
        "\n",
        "output = model.generate(\n",
        "    inputs[\"input_ids\"],\n",
        "    max_new_tokens=200,\n",
        "    do_sample=False,\n",
        ")\n",
        "output = output[0].to(\"cpu\")\n",
        "print(tokenizer.decode(output))\n"
      ],
      "metadata": {
        "id": "hxMINm4vLNFK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441,
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        "outputId": "0ea8be9a-fe2b-4811-ccf3-a51b31c1c146"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/745 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ef46548f9e04a75a229c36f75e5138d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6fd066277ae2494f993e12c6d206ff18"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "2fc42ecf2b2f4ed4bb0746350303c6df"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/411 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "08baf1229b314191b9b8f502ff827316"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)fetensors.index.json:   0%|          | 0.00/25.1k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a0ab6ab17d764c9193a700b9ed312c63"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0c43880962e343e0b4cb601056ca1190"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d52b8f02b7b5489c930a158e6d0435da"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "678a0db0d63c4f29930bf31cf7574c06"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "faa87be1537d4643909a956d67de4ec4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "68985373e09b41b88afdd88456b0d01a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s> <<SYS>>\\nProvide answers in JavaScript\\n<</SYS>>\\n\\nWrite a function that computes the set of sums of all contiguous sublists of a given list.  For example, the list [1, 2, 3, 4, 5] should yield the set [[1, 2, 3, 4, 5], [1, 2, 3, 5], [1, 2, 4], [1, 3], [2, 3, 4], [2, 4], [3, 4], [4], [1, 2], [2], [1]].\\n\\n<</SOLUTION>>\\nfunction sumList(list) {\\n  var result = [];\\n  for (var i = 0; i < list.length; i++) {\\n    var sum = 0;\\n    for (var j = i; j < list.length; j++) {\\n      sum += list[j];\\n      result.push(sum);\\n    }\\n  }\\\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4-bit Loading"
      ],
      "metadata": {
        "id": "3OpErh4GSEg8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "import torch\n",
        "\n",
        "model_id = \"codellama/CodeLlama-34b-hf\"\n",
        "quantization_config = BitsAndBytesConfig(\n",
        "   load_in_4bit=True,\n",
        "   bnb_4bit_compute_dtype=torch.float16\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    quantization_config=quantization_config,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "\n",
        "prompt = 'def remove_non_ascii(s: str) -> str:\\n    \"\"\" '\n",
        "inputs = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "output = model.generate(\n",
        "    inputs[\"input_ids\"],\n",
        "    max_new_tokens=200,\n",
        "    do_sample=True,\n",
        "    top_p=0.9,\n",
        "    temperature=0.1,\n",
        ")\n",
        "output = output[0].to(\"cpu\")\n",
        "print(tokenizer.decode(output))"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 809,
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        "outputId": "d19d317f-2a27-4a21-cdb1-3f2e95ce2805"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
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          "metadata": {}
        },
        {
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          "metadata": {}
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          "metadata": {}
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        {
          "output_type": "display_data",
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "Downloading (…)neration_config.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s> def remove_non_ascii(s: str) -> str:\n",
            "    \"\"\" \n",
            "    Removes non-ascii characters from a string.\n",
            "    \"\"\"\n",
            "    return \"\".join(i for i in s if ord(i) < 128)\n",
            "\n",
            "\n",
            "def remove_non_ascii_from_list(l: list) -> list:\n",
            "    \"\"\" \n",
            "    Removes non-ascii characters from a list of strings.\n",
            "    \"\"\"\n",
            "    return [remove_non_ascii(s) for s in l]\n",
            "</s>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-dDqI7U8PMaE"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
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