consume_chroma_apple_silicon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Special version of Apple Silicon chip for GPU Acceleration (Tested work in MBA M2 2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries.\n",
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "from langchain.vectorstores import Chroma\n",
    "from huggingface_hub import hf_hub_download\n",
    "from langchain.llms import LlamaCpp\n",
    "from langchain.callbacks.manager import CallbackManager\n",
    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
    "from langchain.chains.question_answering import load_qa_chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define to variables to use \"sentence-transformers/all-MiniLM-L6-v2\" embedding model from HuggingFace.\n",
    "# https://huggingface.co/blog/getting-started-with-embeddings\n",
    "# https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2\n",
    "embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Chroma vector store and function to generate embeddings.\n",
    "db = Chroma(persist_directory=\"./chroma_db/\", embedding_function=embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58 Building a superhero team - Get your teammates to read this\n",
      "\n",
      "Congratulations on finishing this book!\n",
      "\n",
      "In Chapter 2, we talked about how this book can help you become the superhero of your team.\n",
      "\n",
      "The only thing better than being a superhero is being part of a superhero team. I hope you’ll give copies of this book to your friends and teammates and help create other superheroes!\n",
      "\n",
      "Page 118\n",
      "\n",
      "Machine Learning Yearning-Draft\n",
      "\n",
      "Andrew Ng\n"
     ]
    }
   ],
   "source": [
    "# Define the query to search and display the most relevant document content.\n",
    "inquiry = \"Who is the author of this book?\"\n",
    "docs = db.similarity_search(inquiry)\n",
    "print(docs[0].page_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting llama-cpp-python\n",
      "  Downloading llama_cpp_python-0.2.6.tar.gz (1.6 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m20.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Installing backend dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hCollecting typing-extensions>=4.5.0 (from llama-cpp-python)\n",
      "  Obtaining dependency information for typing-extensions>=4.5.0 from https://files.pythonhosted.org/packages/24/21/7d397a4b7934ff4028987914ac1044d3b7d52712f30e2ac7a2ae5bc86dd0/typing_extensions-4.8.0-py3-none-any.whl.metadata\n",
      "  Downloading typing_extensions-4.8.0-py3-none-any.whl.metadata (3.0 kB)\n",
      "Collecting numpy>=1.20.0 (from llama-cpp-python)\n",
      "  Obtaining dependency information for numpy>=1.20.0 from https://files.pythonhosted.org/packages/35/21/9e150d654da358beb29fe216f339dc17f2b2ac13fff2a89669401a910550/numpy-1.26.0-cp311-cp311-macosx_11_0_arm64.whl.metadata\n",
      "  Downloading numpy-1.26.0-cp311-cp311-macosx_11_0_arm64.whl.metadata (99 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.7/99.7 kB\u001b[0m \u001b[31m33.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hCollecting diskcache>=5.6.1 (from llama-cpp-python)\n",
      "  Obtaining dependency information for diskcache>=5.6.1 from https://files.pythonhosted.org/packages/3f/27/4570e78fc0bf5ea0ca45eb1de3818a23787af9b390c0b0a0033a1b8236f9/diskcache-5.6.3-py3-none-any.whl.metadata\n",
      "  Downloading diskcache-5.6.3-py3-none-any.whl.metadata (20 kB)\n",
      "Downloading diskcache-5.6.3-py3-none-any.whl (45 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.5/45.5 kB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading numpy-1.26.0-cp311-cp311-macosx_11_0_arm64.whl (14.0 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.0/14.0 MB\u001b[0m \u001b[31m25.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hDownloading typing_extensions-4.8.0-py3-none-any.whl (31 kB)\n",
      "Building wheels for collected packages: llama-cpp-python\n",
      "  Building wheel for llama-cpp-python (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for llama-cpp-python: filename=llama_cpp_python-0.2.6-cp311-cp311-macosx_13_0_arm64.whl size=851307 sha256=83c75d2cedaa3e9db1d67f5d3c998aa78a82e579112828d654ffd7139845f175\n",
      "  Stored in directory: /private/var/folders/s5/d5_419x12qnbmsszkklmtwb40000gn/T/pip-ephem-wheel-cache-ne_f13wf/wheels/18/f3/e6/e6d374c76db44b5b0451c3a76b3049f29e881819bc43f53d4d\n",
      "Successfully built llama-cpp-python\n",
      "Installing collected packages: typing-extensions, numpy, diskcache, llama-cpp-python\n",
      "  Attempting uninstall: typing-extensions\n",
      "    Found existing installation: typing_extensions 4.7.1\n",
      "    Uninstalling typing_extensions-4.7.1:\n",
      "      Successfully uninstalled typing_extensions-4.7.1\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: diskcache\n",
      "    Found existing installation: diskcache 5.6.3\n",
      "    Uninstalling diskcache-5.6.3:\n",
      "      Successfully uninstalled diskcache-5.6.3\n",
      "  Attempting uninstall: llama-cpp-python\n",
      "    Found existing installation: llama_cpp_python 0.2.6\n",
      "    Uninstalling llama_cpp_python-0.2.6:\n",
      "      Successfully uninstalled llama_cpp_python-0.2.6\n",
      "Successfully installed diskcache-5.6.3 llama-cpp-python-0.2.6 numpy-1.26.0 typing-extensions-4.8.0\n"
     ]
    }
   ],
   "source": [
    "# Install llama-cpp-python library to use llama.cpp for Apple Silicon.\n",
    "# https://python.langchain.com/docs/integrations/llms/llamacpp\n",
    "# !CMAKE_ARGS=\"-DLLAMA_METAL=on\" FORCE_CMAKE=1 pip install --upgrade --force-reinstall llama-cpp-python --no-cache-dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defien the Llama 2 model name, path, and base name to use.\n",
    "# https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML\n",
    "model_name_or_path = \"TheBloke/Llama-2-13B-chat-GGUF\"\n",
    "model_basename = \"llama-2-13b-chat.Q4_K_M.gguf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construst the model path and download the model from HuggingFace.\n",
    "model_path = hf_hub_download(repo_id=model_name_or_path, filename=model_basename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/easonlai/.cache/huggingface/hub/models--TheBloke--Llama-2-13B-chat-GGUF/snapshots/245bc5104d85dcc9a11a0e0a9ae6de38dfae536f/llama-2-13b-chat.Q4_K_M.gguf\n"
     ]
    }
   ],
   "source": [
    "# Print out the model path.\n",
    "print(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Callbacks support token-wise streaming.\n",
    "callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "llama_model_loader: loaded meta data with 19 key-value pairs and 363 tensors from /Users/easonlai/.cache/huggingface/hub/models--TheBloke--Llama-2-13B-chat-GGUF/snapshots/245bc5104d85dcc9a11a0e0a9ae6de38dfae536f/llama-2-13b-chat.Q4_K_M.gguf (version GGUF V2 (latest))\n",
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      "llama_model_loader: - tensor   84:           blk.4.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
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      "llama_model_loader: - tensor   87:              blk.4.ffn_up.weight q4_K     [  5120, 13824,     1,     1 ]\n",
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      "llama_model_loader: - tensor  138:          blk.15.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
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      "llama_model_loader: - tensor  226:          blk.25.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
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      "llama_model_loader: - tensor  235:          blk.26.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  236:           blk.26.ffn_down.weight q4_K     [ 13824,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  237:           blk.26.ffn_gate.weight q4_K     [  5120, 13824,     1,     1 ]\n",
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      "llama_model_loader: - tensor  244:          blk.27.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  245:           blk.27.ffn_down.weight q4_K     [ 13824,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  246:           blk.27.ffn_gate.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  247:             blk.27.ffn_up.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  248:           blk.27.ffn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  249:             blk.27.attn_k.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  250:        blk.27.attn_output.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  251:             blk.27.attn_q.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  252:             blk.27.attn_v.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  253:          blk.28.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  254:           blk.28.ffn_down.weight q6_K     [ 13824,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  255:           blk.28.ffn_gate.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  256:             blk.28.ffn_up.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  257:           blk.28.ffn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  258:             blk.28.attn_k.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  259:        blk.28.attn_output.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  260:             blk.28.attn_q.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  261:             blk.28.attn_v.weight q6_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  262:          blk.29.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  263:           blk.29.ffn_down.weight q4_K     [ 13824,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  264:           blk.29.ffn_gate.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  265:             blk.29.ffn_up.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  266:           blk.29.ffn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  267:             blk.29.attn_k.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  268:        blk.29.attn_output.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  269:             blk.29.attn_q.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  270:             blk.29.attn_v.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  271:           blk.30.ffn_gate.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  272:             blk.30.ffn_up.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  273:             blk.30.attn_k.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  274:        blk.30.attn_output.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  275:             blk.30.attn_q.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  276:             blk.30.attn_v.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  277:                    output.weight q6_K     [  5120, 32000,     1,     1 ]\n",
      "llama_model_loader: - tensor  278:          blk.30.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  279:           blk.30.ffn_down.weight q4_K     [ 13824,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  280:           blk.30.ffn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  281:          blk.31.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  282:           blk.31.ffn_down.weight q6_K     [ 13824,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  283:           blk.31.ffn_gate.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  284:             blk.31.ffn_up.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  285:           blk.31.ffn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  286:             blk.31.attn_k.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  287:        blk.31.attn_output.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  288:             blk.31.attn_q.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  289:             blk.31.attn_v.weight q6_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  290:          blk.32.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  291:           blk.32.ffn_down.weight q4_K     [ 13824,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  292:           blk.32.ffn_gate.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  293:             blk.32.ffn_up.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  294:           blk.32.ffn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  295:             blk.32.attn_k.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  296:        blk.32.attn_output.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  297:             blk.32.attn_q.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  298:             blk.32.attn_v.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  299:          blk.33.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  300:           blk.33.ffn_down.weight q4_K     [ 13824,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  301:           blk.33.ffn_gate.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  302:             blk.33.ffn_up.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  303:           blk.33.ffn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  304:             blk.33.attn_k.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  305:        blk.33.attn_output.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  306:             blk.33.attn_q.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  307:             blk.33.attn_v.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  308:          blk.34.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  309:           blk.34.ffn_down.weight q6_K     [ 13824,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  310:           blk.34.ffn_gate.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  311:             blk.34.ffn_up.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  312:           blk.34.ffn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  313:             blk.34.attn_k.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  314:        blk.34.attn_output.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  315:             blk.34.attn_q.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  316:             blk.34.attn_v.weight q6_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  317:          blk.35.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  318:           blk.35.ffn_down.weight q6_K     [ 13824,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  319:           blk.35.ffn_gate.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  320:             blk.35.ffn_up.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  321:           blk.35.ffn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  322:             blk.35.attn_k.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  323:        blk.35.attn_output.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  324:             blk.35.attn_q.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  325:             blk.35.attn_v.weight q6_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  326:          blk.36.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  327:           blk.36.ffn_down.weight q6_K     [ 13824,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  328:           blk.36.ffn_gate.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  329:             blk.36.ffn_up.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  330:           blk.36.ffn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  331:             blk.36.attn_k.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  332:        blk.36.attn_output.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  333:             blk.36.attn_q.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  334:             blk.36.attn_v.weight q6_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  335:          blk.37.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  336:           blk.37.ffn_down.weight q6_K     [ 13824,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  337:           blk.37.ffn_gate.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  338:             blk.37.ffn_up.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  339:           blk.37.ffn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  340:             blk.37.attn_k.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  341:        blk.37.attn_output.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  342:             blk.37.attn_q.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  343:             blk.37.attn_v.weight q6_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  344:          blk.38.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  345:           blk.38.ffn_down.weight q6_K     [ 13824,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  346:           blk.38.ffn_gate.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  347:             blk.38.ffn_up.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  348:           blk.38.ffn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  349:             blk.38.attn_k.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  350:        blk.38.attn_output.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  351:             blk.38.attn_q.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  352:             blk.38.attn_v.weight q6_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  353:          blk.39.attn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  354:           blk.39.ffn_down.weight q6_K     [ 13824,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  355:           blk.39.ffn_gate.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  356:             blk.39.ffn_up.weight q4_K     [  5120, 13824,     1,     1 ]\n",
      "llama_model_loader: - tensor  357:           blk.39.ffn_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  358:             blk.39.attn_k.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  359:        blk.39.attn_output.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  360:             blk.39.attn_q.weight q4_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  361:             blk.39.attn_v.weight q6_K     [  5120,  5120,     1,     1 ]\n",
      "llama_model_loader: - tensor  362:               output_norm.weight f32      [  5120,     1,     1,     1 ]\n",
      "llama_model_loader: - kv   0:                       general.architecture str     \n",
      "llama_model_loader: - kv   1:                               general.name str     \n",
      "llama_model_loader: - kv   2:                       llama.context_length u32     \n",
      "llama_model_loader: - kv   3:                     llama.embedding_length u32     \n",
      "llama_model_loader: - kv   4:                          llama.block_count u32     \n",
      "llama_model_loader: - kv   5:                  llama.feed_forward_length u32     \n",
      "llama_model_loader: - kv   6:                 llama.rope.dimension_count u32     \n",
      "llama_model_loader: - kv   7:                 llama.attention.head_count u32     \n",
      "llama_model_loader: - kv   8:              llama.attention.head_count_kv u32     \n",
      "llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32     \n",
      "llama_model_loader: - kv  10:                          general.file_type u32     \n",
      "llama_model_loader: - kv  11:                       tokenizer.ggml.model str     \n",
      "llama_model_loader: - kv  12:                      tokenizer.ggml.tokens arr     \n",
      "llama_model_loader: - kv  13:                      tokenizer.ggml.scores arr     \n",
      "llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr     \n",
      "llama_model_loader: - kv  15:                tokenizer.ggml.bos_token_id u32     \n",
      "llama_model_loader: - kv  16:                tokenizer.ggml.eos_token_id u32     \n",
      "llama_model_loader: - kv  17:            tokenizer.ggml.unknown_token_id u32     \n",
      "llama_model_loader: - kv  18:               general.quantization_version u32     \n",
      "llama_model_loader: - type  f32:   81 tensors\n",
      "llama_model_loader: - type q4_K:  241 tensors\n",
      "llama_model_loader: - type q6_K:   41 tensors\n",
      "llm_load_print_meta: format         = GGUF V2 (latest)\n",
      "llm_load_print_meta: arch           = llama\n",
      "llm_load_print_meta: vocab type     = SPM\n",
      "llm_load_print_meta: n_vocab        = 32000\n",
      "llm_load_print_meta: n_merges       = 0\n",
      "llm_load_print_meta: n_ctx_train    = 4096\n",
      "llm_load_print_meta: n_ctx          = 1024\n",
      "llm_load_print_meta: n_embd         = 5120\n",
      "llm_load_print_meta: n_head         = 40\n",
      "llm_load_print_meta: n_head_kv      = 40\n",
      "llm_load_print_meta: n_layer        = 40\n",
      "llm_load_print_meta: n_rot          = 128\n",
      "llm_load_print_meta: n_gqa          = 1\n",
      "llm_load_print_meta: f_norm_eps     = 1.0e-05\n",
      "llm_load_print_meta: f_norm_rms_eps = 1.0e-05\n",
      "llm_load_print_meta: n_ff           = 13824\n",
      "llm_load_print_meta: freq_base      = 10000.0\n",
      "llm_load_print_meta: freq_scale     = 1\n",
      "llm_load_print_meta: model type     = 13B\n",
      "llm_load_print_meta: model ftype    = mostly Q4_K - Medium\n",
      "llm_load_print_meta: model size     = 13.02 B\n",
      "llm_load_print_meta: general.name   = LLaMA v2\n",
      "llm_load_print_meta: BOS token = 1 '<s>'\n",
      "llm_load_print_meta: EOS token = 2 '</s>'\n",
      "llm_load_print_meta: UNK token = 0 '<unk>'\n",
      "llm_load_print_meta: LF token  = 13 '<0x0A>'\n",
      "llm_load_tensors: ggml ctx size =    0.12 MB\n",
      "llm_load_tensors: mem required  = 7500.97 MB (+  800.00 MB per state)\n",
      "....................................................................................................\n",
      "llama_new_context_with_model: kv self size  =  800.00 MB\n",
      "ggml_metal_init: allocating\n",
      "ggml_metal_init: found device: Apple M2\n",
      "ggml_metal_init: picking default device: Apple M2\n",
      "ggml_metal_init: loading '/Users/easonlai/anaconda3/envs/llama2/lib/python3.11/site-packages/llama_cpp/ggml-metal.metal'\n",
      "ggml_metal_init: loaded kernel_add                            0x2a2144cb0 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_add_row                        0x2a2144630 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul                            0x2a2147320 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_row                        0x2a214ae20 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_scale                          0x2a21478e0 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_silu                           0x2a2149370 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_relu                           0x2a214f500 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_gelu                           0x2a214f760 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_soft_max                       0x2a214f9c0 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_soft_max_4                     0x2a214fc20 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_diag_mask_inf                  0x2a214fe80 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_diag_mask_inf_8                0x2a21500e0 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_get_rows_f32                   0x2a2150340 | th_max =  896 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_get_rows_f16                   0x2a21505a0 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_get_rows_q4_0                  0x2a2150800 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_get_rows_q4_1                  0x2a2150a60 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_get_rows_q8_0                  0x2a2150cc0 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_get_rows_q2_K                  0x2a2150f20 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_get_rows_q3_K                  0x2a2151180 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_get_rows_q4_K                  0x2a21513e0 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_get_rows_q5_K                  0x2a2151640 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_get_rows_q6_K                  0x2a21518a0 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_rms_norm                       0x2a2151b00 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_norm                           0x2a2151d60 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mat_f32_f32                0x2a2151fc0 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mat_f16_f32                0x2a2152220 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mat_f16_f32_1row           0x2a2152480 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mat_f16_f32_l4             0x2a21526e0 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32               0x2a2152940 | th_max =  896 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32               0x2a2152ba0 | th_max =  896 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mat_q8_0_f32               0x2a2152e00 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mat_q2_K_f32               0x2a2153060 | th_max =  640 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mat_q3_K_f32               0x2a21532c0 | th_max =  576 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mat_q4_K_f32               0x2a2153520 | th_max =  576 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mat_q5_K_f32               0x2a2153780 | th_max =  640 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mat_q6_K_f32               0x2a21539e0 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mm_f32_f32                 0x2a2153c40 | th_max =  768 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mm_f16_f32                 0x2a2153ea0 | th_max =  768 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mm_q4_0_f32                0x2a2154100 | th_max =  768 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mm_q8_0_f32                0x2a2154360 | th_max =  768 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mm_q4_1_f32                0x2a21545c0 | th_max =  768 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mm_q2_K_f32                0x2a2154820 | th_max =  768 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mm_q3_K_f32                0x2a2154a80 | th_max =  768 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mm_q4_K_f32                0x2a2154ce0 | th_max =  768 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mm_q5_K_f32                0x2a2154f40 | th_max =  768 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_mul_mm_q6_K_f32                0x2a21551a0 | th_max =  768 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_rope                           0x2a2155400 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_alibi_f32                      0x2a2155660 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_cpy_f32_f16                    0x2a21558c0 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_cpy_f32_f32                    0x2a2155b20 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: loaded kernel_cpy_f16_f16                    0x2a2155d80 | th_max = 1024 | th_width =   32\n",
      "ggml_metal_init: hasUnifiedMemory              = true\n",
      "ggml_metal_init: recommendedMaxWorkingSetSize  = 10922.67 MB\n",
      "ggml_metal_init: maxTransferRate               = built-in GPU\n",
      "llama_new_context_with_model: compute buffer total size =  131.47 MB\n",
      "llama_new_context_with_model: max tensor size =   128.17 MB\n",
      "ggml_metal_add_buffer: allocated 'data            ' buffer, size =  7501.56 MB, (15942.88 / 10922.67), warning: current allocated size is greater than the recommended max working set size\n",
      "ggml_metal_add_buffer: allocated 'eval            ' buffer, size =     1.48 MB, (15944.36 / 10922.67), warning: current allocated size is greater than the recommended max working set size\n",
      "ggml_metal_add_buffer: allocated 'kv              ' buffer, size =   802.00 MB, (16746.36 / 10922.67), warning: current allocated size is greater than the recommended max working set size\n",
      "ggml_metal_add_buffer: allocated 'alloc           ' buffer, size =   130.02 MB, (16876.38 / 10922.67), warning: current allocated size is greater than the recommended max working set size\n"
     ]
    }
   ],
   "source": [
    "# Reference configuration for GPU https://python.langchain.com/docs/integrations/llms/llamacpp#metal.\n",
    "n_gpu_layers = 1  # Metal set to 1 is enough.\n",
    "n_batch = 512  # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.\n",
    "\n",
    "# Initialize the llamaCpp model.\n",
    "llm = LlamaCpp(\n",
    "    model_path=model_path,\n",
    "    max_tokens=256,\n",
    "    n_gpu_layers=n_gpu_layers,\n",
    "    n_batch=n_batch,\n",
    "    f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls\n",
    "    callback_manager=callback_manager,\n",
    "    n_ctx=1024,\n",
    "    verbose=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the question answering chain.\n",
    "chain = load_qa_chain(llm, chain_type=\"stuff\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The author of this book is Andrew Ng."
     ]
    },
    {
     "data": {
      "text/plain": [
       "' The author of this book is Andrew Ng.'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform first sample of question answering.\n",
    "inquiry = \"Who is the author of this book?\"\n",
    "docs = db.similarity_search(inquiry)\n",
    "chain.run(input_documents=docs, question=inquiry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " This book is a guide to help you use Machine Learning for real-world projects, especially those that involve deep learning and scaling up your models. The book covers how to set technical direction and avoid common mistakes."
     ]
    },
    {
     "data": {
      "text/plain": [
       "' This book is a guide to help you use Machine Learning for real-world projects, especially those that involve deep learning and scaling up your models. The book covers how to set technical direction and avoid common mistakes.'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform second sample of question answering.\n",
    "inquiry = \"Please tell me the key summary of this book.\"\n",
    "docs = db.similarity_search(inquiry)\n",
    "chain.run(input_documents=docs, question=inquiry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "llama2",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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