stablelm_zephyr_3b_feature_test.ipynb

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        "<a href=\"https://colab.research.google.com/github/Adnan525/2048python/blob/master/stablelm_zephyr_3b_feature_test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Feature Test for stablelm-zephyr-3b\n",
        "\n",
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        "\n",
        "Here's the [release post](https://stability.ai/news/stablelm-zephyr-3b-stability-llm)."
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            "Building wheels for collected packages: tinysegmenter, spider-client, feedfinder2, jieba3k, sgmllib3k\n",
            "  Building wheel for tinysegmenter (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Successfully built tinysegmenter spider-client feedfinder2 jieba3k sgmllib3k\n",
            "Installing collected packages: tinysegmenter, striprtf, sgmllib3k, jieba3k, dirtyjson, pypdf, pyee, outcome, 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, mypy-extensions, minijinja, marshmallow, html2text, h11, feedparser, deprecated, cssselect, chromedriver-autoinstaller, wsproto, typing-inspect, trio, tiktoken, spider-client, requests-file, playwright, nvidia-cusparse-cu12, nvidia-cudnn-cu12, huggingface-hub, httpcore, feedfinder2, trio-websocket, tldextract, text-generation, nvidia-cusolver-cu12, httpx, dataclasses-json, selenium, openai, newspaper3k, llamaindex-py-client, sentence-transformers, llama-index-legacy, llama-index-core, bitsandbytes, accelerate, llama-parse, llama-index-readers-web, llama-index-readers-file, llama-index-llms-openai, llama-index-indices-managed-llama-cloud, llama-index-embeddings-openai, llama-index-embeddings-huggingface, llama-index-readers-llama-parse, llama-index-multi-modal-llms-openai, llama-index-llms-huggingface, llama-index-cli, llama-index-agent-openai, llama-index-program-openai, llama-index-question-gen-openai, llama-index\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.20.3\n",
            "    Uninstalling huggingface-hub-0.20.3:\n",
            "      Successfully uninstalled huggingface-hub-0.20.3\n",
            "Successfully installed accelerate-0.30.1 bitsandbytes-0.43.1 chromedriver-autoinstaller-0.6.4 cssselect-1.2.0 dataclasses-json-0.6.6 deprecated-1.2.14 dirtyjson-1.0.8 feedfinder2-0.0.4 feedparser-6.0.11 h11-0.14.0 html2text-2020.1.16 httpcore-1.0.5 httpx-0.27.0 huggingface-hub-0.23.0 jieba3k-0.35.1 llama-index-0.10.36 llama-index-agent-openai-0.2.4 llama-index-cli-0.1.12 llama-index-core-0.10.36 llama-index-embeddings-huggingface-0.2.0 llama-index-embeddings-openai-0.1.9 llama-index-indices-managed-llama-cloud-0.1.6 llama-index-legacy-0.9.48 llama-index-llms-huggingface-0.2.0 llama-index-llms-openai-0.1.19 llama-index-multi-modal-llms-openai-0.1.6 llama-index-program-openai-0.1.6 llama-index-question-gen-openai-0.1.3 llama-index-readers-file-0.1.22 llama-index-readers-llama-parse-0.1.4 llama-index-readers-web-0.1.14 llama-parse-0.4.2 llamaindex-py-client-0.1.19 marshmallow-3.21.2 minijinja-2.0.1 mypy-extensions-1.0.0 newspaper3k-0.2.8 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-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 openai-1.30.1 outcome-1.3.0.post0 playwright-1.43.0 pyee-11.1.0 pypdf-4.2.0 requests-file-2.0.0 selenium-4.20.0 sentence-transformers-2.7.0 sgmllib3k-1.0.0 spider-client-0.0.11 striprtf-0.0.26 text-generation-0.7.0 tiktoken-0.7.0 tinysegmenter-0.3 tldextract-5.1.2 trio-0.25.0 trio-websocket-0.11.1 typing-inspect-0.9.0 wsproto-1.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "q1_IZvrVM_FQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data"
      ],
      "metadata": {
        "id": "kkjVukvQO_5D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p \"data/java/\""
      ],
      "metadata": {
        "id": "quYBhKouOIxM"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core import SimpleDirectoryReader\n",
        "\n",
        "documents = SimpleDirectoryReader(\"data/java\").load_data()"
      ],
      "metadata": {
        "id": "jBG7NvF9UzPj"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### LLM\n",
        "\n",
        "This should run on a T4 instance on the free tier"
      ],
      "metadata": {
        "id": "zXOJQa7FOGr5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "lMNaHDzPM68f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395,
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            "7c2b4fcdef994eecaf98e5d9015d0e10",
            "1ab04ecc0c374289ad0f74997b0524fa"
          ]
        },
        "outputId": "c69ae616-17f4-4cc1-9269-579e7e23ae01"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pydantic/_internal/_fields.py:160: UserWarning: Field \"model_id\" has conflict with protected namespace \"model_\".\n",
            "\n",
            "You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/599 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "78c905b2c2204e3391ad2a20a7c1963b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/5.59G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e0582f5e1bdd4d1a9c7202194175af66"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d22c55df6bde4387bdcb0f3a982abd1b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/5.21k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "477bd7af027b4f5c83e127cb366f2413"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a48a931fe19244a6915f25d162886769"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/587 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3be1fdddc683454db301efe2eb9ad7d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import BitsAndBytesConfig\n",
        "from llama_index.core.prompts import PromptTemplate\n",
        "from llama_index.llms.huggingface import HuggingFaceLLM\n",
        "\n",
        "quantization_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_use_double_quant=True,\n",
        ")\n",
        "\n",
        "\n",
        "def messages_to_prompt(messages):\n",
        "  prompt = \"\"\n",
        "  for message in messages:\n",
        "    if message.role == 'system':\n",
        "      prompt += f\"<|system|>\\n{message.content}<|endoftext|>\\n\"\n",
        "    elif message.role == 'user':\n",
        "      prompt += f\"<|user|>\\n{message.content}<|endoftext|>\\n\"\n",
        "    elif message.role == 'assistant':\n",
        "      prompt += f\"<|assistant|>\\n{message.content}<|endoftext|>\\n\"\n",
        "\n",
        "  # ensure we start with a system prompt, insert blank if needed\n",
        "  if not prompt.startswith(\"<|system|>\\n\"):\n",
        "    prompt = \"<|system|>\\n<|endoftext|>\\n\" + prompt\n",
        "\n",
        "  # add final assistant prompt\n",
        "  prompt = prompt + \"<|assistant|>\\n\"\n",
        "\n",
        "  return prompt\n",
        "\n",
        "\n",
        "llm = HuggingFaceLLM(\n",
        "    model_name=\"stabilityai/stablelm-zephyr-3b\",\n",
        "    tokenizer_name=\"stabilityai/stablelm-zephyr-3b\",\n",
        "    query_wrapper_prompt=PromptTemplate(\"<|system|>\\n<|endoftext|>\\n<|user|>\\n{query_str}<|endoftext|>\\n<|assistant|>\\n\"),\n",
        "    context_window=3900,\n",
        "    max_new_tokens=256,\n",
        "    model_kwargs={\"quantization_config\": quantization_config},\n",
        "    # tokenizer_kwargs={},\n",
        "    generate_kwargs={\"temperature\": 0.8},\n",
        "    messages_to_prompt=messages_to_prompt,\n",
        "    device_map=\"auto\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core import Settings\n",
        "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
        "\n",
        "Settings.llm = llm\n",
        "Settings.embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")"
      ],
      "metadata": {
        "id": "rPawBPL0SwFj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369,
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        },
        "outputId": "b23012c6-2607-45ab-8918-a6ed007303ed"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d5d443b78ca4ce8b15df3723dea5138"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "769b24bf5e3342d4aca0f24e108d862a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/94.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f1b61f26dee478b93e8090036234ed4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f5f98e1f2a824862b65c4301638903c3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/743 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c362cc7130ac44ebbb4a4c3b095413d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/133M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fc56912bcbb54bdda22411a3cbaa4fa0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/366 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "93fcd8908ace493c92b9e15c57b187c0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "249afa151ee14a42b42c2fc5bc615f56"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "716c4fc97ec0402286ab7a42ef203727"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "06fbaae2a7304678a577d646daa4c8e8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "394c138ddd0c4e11b5ff08d4d3450c58"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Index Setup"
      ],
      "metadata": {
        "id": "_iYeofUlS6_D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core import VectorStoreIndex\n",
        "\n",
        "vector_index = VectorStoreIndex.from_documents(documents)"
      ],
      "metadata": {
        "id": "Jz173lnJS8cZ"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core import SummaryIndex\n",
        "\n",
        "summary_index = SummaryIndex.from_documents(documents)"
      ],
      "metadata": {
        "id": "CuGwYVl5S-GH"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Helpful Imports / Logging"
      ],
      "metadata": {
        "id": "pxFrAm_bTCYY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.core.response.notebook_utils import display_response"
      ],
      "metadata": {
        "id": "ghqk6C04TD3b"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import logging\n",
        "import sys\n",
        "\n",
        "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
        "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
      ],
      "metadata": {
        "id": "6YVB1ny-UjN4"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Basic Query Engine"
      ],
      "metadata": {
        "id": "K0l3oUS8NO-I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Compact (default)"
      ],
      "metadata": {
        "id": "TC2Vdq5LNe6H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine = vector_index.as_query_engine(response_mode=\"compact\")\n",
        "\n",
        "response = query_engine.query(\"the author suggested the next big revolution will be in what?\")\n",
        "\n",
        "display_response(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "id": "Lb9CsvvQNR9K",
        "outputId": "b33f74b0-4cc6-4e46-8b45-50700b72142d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (2126 > 2048). Running this sequence through the model will result in indexing errors\n",
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**`Final Response:`** The author suggested the next big revolution will be in genetic engineering."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Refine"
      ],
      "metadata": {
        "id": "p8WNEWO6NeEJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine = vector_index.as_query_engine(response_mode=\"refine\")\n",
        "\n",
        "response = query_engine.query(\"the author suggested the next big revolution will be in what?\")\n",
        "\n",
        "display_response(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "id": "91CZsrtjNi4I",
        "outputId": "fdd99eb8-13c9-4166-f23c-fc1402e99db5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**`Final Response:`** The author suggested that the next big revolution will be in genetic engineering. This is in line with the context provided, which discusses the inspiration for the cover design of the book and the author's perspective on Java as an attempt to elevate the programmer from being just a mechanic to a \"software craftsman.\" While the context is not directly related to the topic of revolutions, it highlights the author's perspective on the evolution of technology and the importance of being a skilled craftsman in the digital age."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tree Summarize"
      ],
      "metadata": {
        "id": "EBr8vyabNSmK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine = vector_index.as_query_engine(response_mode=\"tree_summarize\")\n",
        "\n",
        "response = query_engine.query(\"the author suggested the next big revolution will be in what?\")\n",
        "\n",
        "display_response(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        },
        "id": "X3YTKZXVNZ0R",
        "outputId": "7d7a7e19-6229-40a9-9db8-b8b394639bed"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**`Final Response:`** The author suggested the next big revolution will be in genetic engineering."
          },
          "metadata": {}
        }
      ]
    }
  ]
}