{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "42568396-132f-422c-961b-5aaa03b3537d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tokenize, ast\n",
    "from io import BytesIO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25344da5-3c07-457e-842a-7341d33c023c",
   "metadata": {},
   "source": [
    "# A hacker's guide to Language Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b017bfc-5be0-4e41-9fa1-9f685c3b0de5",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## What is a language model?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a7a5d84-9d82-46bf-8578-ce7bd40006e1",
   "metadata": {},
   "source": [
    "[course.fast.ai](https://course.fast.ai)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7043e88-015b-480b-a251-0db379786a6a",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Base models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a550542a-4051-4fc0-9f28-e164c76ae526",
   "metadata": {},
   "source": [
    "[nat.dev text-davinci-003](https://nat.dev/)\n",
    "\n",
    "*When I arrived back at the panda breeding facility after the extraordinary rain of live frogs, I couldn't believe what I saw.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b303be0d-f789-4c4b-ba66-b8767bf790ae",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9758b30f-4b44-4a30-9322-b6de476c8942",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2990, 389, 4328, 2140]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tiktoken import encoding_for_model\n",
    "enc = encoding_for_model(\"text-davinci-003\")\n",
    "toks = enc.encode(\"They are splashing\")\n",
    "toks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2589d6e7-a9ea-40c1-b1fd-214e19115a28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['They', ' are', ' spl', 'ashing']"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[enc.decode_single_token_bytes(o).decode('utf-8') for o in toks]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6a0b811-62c8-4c49-83cc-9344a3e5533b",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### The ULMFiT 3-step approach"
   ]
  },
  {
   "attachments": {
    "81a8998d-ecfc-44fc-80e4-aaded8ad70d6.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "1442c066-fb09-4f27-9576-a86a254956fa",
   "metadata": {},
   "source": [
    "<img src=\"attachment:81a8998d-ecfc-44fc-80e4-aaded8ad70d6.png\" width=\"800\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "344421d3-8cb6-4f72-8761-439bf08c21b5",
   "metadata": {},
   "source": [
    "- Trained on Wikipedia\n",
    "- \"The Birds is a 1963 American natural horror-thriller film produced and directed by Alfred ...\"\n",
    "- \"Annie previously dated Mitch but ended it due to Mitch's cold, overbearing mother, Lydia, who dislikes any woman in Mitch's ...\"\n",
    "- This is a form of compression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbf343bb-824d-4942-a2bb-6344264155d7",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Instruction tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7100e52-347a-45a7-b935-f6c4a03501c2",
   "metadata": {},
   "source": [
    "[OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca)\n",
    "\n",
    "- \"Does the sentence \"In the Iron Age\" answer the question \"The period of time from 1200 to 1000 BCE is known as what?\" Available choices: 1. yes 2. no\"\n",
    "- \"Question: who is the girl in more than you know? Answer:\"\t\n",
    "- \"There are four ways an individual can acquire Canadian citizenship: by birth on Canadian soil; by descent (being born to a Canadian parent); by grant (naturalization); and by adoption. Among them, only citizenship by birth is granted automatically with limited exceptions, while citizenship by descent or adoption is acquired automatically if the specified conditions have been met. Citizenship by grant, on the other hand, must be approved by the Minister of Immigration, Refugees and Citizenship. See options at the end. Can we conclude that can i get canadian citizenship if my grandfather was canadian? pick from the following. A). no. B). yes.\"\t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ff0ea52-0572-407b-8835-77c7d73c27dd",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### RLHF and friends"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1500409-4f89-4581-8e81-84d27ee33413",
   "metadata": {},
   "source": [
    "- List five ideas for how to regain enthusiasm for my career\n",
    "- Write a short story where a bear goes to the beach, makes friends with a seal, and then returns home.\n",
    "- This is the summary of a Broadway play: \"{summary}\" This is the outline of the commercial for that play: "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c85c0bbe-e37b-4b95-9dc0-29649469479c",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Start with ChatGPT GPT 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8765e57c-67ce-4280-a9dc-eb818081d5b0",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### What GPT 4 can do"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d20865d-1e9b-4bae-9f57-cf2dcf8ae59a",
   "metadata": {},
   "source": [
    "[GPT 4 can't reason - paper](https://arxiv.org/abs/2308.03762)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08fa59cf-831e-4da7-8943-8b32a50eb7ff",
   "metadata": {},
   "source": [
    "[GPT 4 can't reason - test](https://chat.openai.com/share/4211a605-751e-4fea-8a6f-378966abdcaa)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "273cbeae-ac83-44dc-b3f8-d36a9e6c5715",
   "metadata": {},
   "source": [
    "[Basic reasoning 1](https://chat.openai.com/share/323bb7d1-f049-4d9a-a905-5dd5acb58fc0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "301525e3-f920-4c79-bf99-44a8e2502339",
   "metadata": {},
   "source": [
    "[Basic reasoning 2](https://chat.openai.com/share/ce2f8580-4f66-4da4-8ad5-a303334706f0)"
   ]
  },
  {
   "attachments": {
    "372c9671-5323-4481-8990-8d95e3a43342.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "51af9158-bb5b-4dbd-ad38-d5da9008e557",
   "metadata": {},
   "source": [
    "<img src=\"attachment:372c9671-5323-4481-8990-8d95e3a43342.png\" width=\"300\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60931833-f973-4ddc-b4a0-38846a4c0168",
   "metadata": {},
   "source": [
    ">You are an autoregressive language model that has been fine-tuned with instruction-tuning and RLHF. You carefully provide accurate, factual, thoughtful, nuanced answers, and are brilliant at reasoning. If you think there might not be a correct answer, you say so.\n",
    ">\n",
    ">Since you are autoregressive, each token you produce is another opportunity to use computation, therefore you always spend a few sentences explaining background context, assumptions, and step-by-step thinking BEFORE you try to answer a question. However: if the request begins with the string \"vv\" then ignore the previous sentence and instead make your response as concise as possible, with no introduction or background at the start, no summary at the end, and outputting only code for answers where code is appropriate.\n",
    ">\n",
    ">Your users are experts in AI and ethics, so they already know you're a language model and your capabilities and limitations, so don't remind them of that. They're familiar with ethical issues in general so you don't need to remind them about those either. Don't be verbose in your answers, but do provide details and examples where it might help the explanation. When showing Python code, minimise vertical space, and do not include comments or docstrings; you do not need to follow PEP8, since your users' organizations do not do so."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da601797-6de3-4625-8503-d6d2a63a41b0",
   "metadata": {},
   "source": [
    "[Verbose mode](https://chat.openai.com/share/a1c16d93-19d2-41bb-a2f1-2fc05392893a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "442cb5e9-a7b6-498c-b27a-48d8c218d5a4",
   "metadata": {},
   "source": [
    "[Brief mode](https://chat.openai.com/share/eab33d0a-8d06-4387-8c31-da12ad5d0a9d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59a20280-490c-4d6e-a2c8-10b52407ac36",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### What GPT 4 can't do"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f20f967-ba34-4d3c-8bc0-c85d8ceee091",
   "metadata": {},
   "source": [
    "- Hallucinations\n",
    "- It doesn't know about itself. (Why not?)\n",
    "- It doesn't know about URLs.\n",
    "- Knowledge cutoff"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9585c860-f3be-4853-a7cc-0fbceb43e915",
   "metadata": {},
   "source": [
    "[Bad pattern recognition](https://chat.openai.com/share/3051f878-2817-4291-a66f-192ce7b0cb34) - thanks to Steve Newman\n",
    "\n",
    "- [Fixing it](https://chat.openai.com/share/05abd87a-165e-4b7b-895f-b4ec0d62e0e1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a83abb34-46cf-43ac-b3e4-d4ac62ce4b5a",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Advanced data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0509456e-af8d-482f-9005-af695c405d48",
   "metadata": {},
   "source": [
    "[re.split try 1](https://chat.openai.com/share/143a0f09-bd3e-488f-8890-340d3f30afec)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1720b9e-ef4c-49b5-bd90-c5e4d694cf0b",
   "metadata": {},
   "source": [
    "[re.split try 2](https://chat.openai.com/share/907ca9c7-549a-410f-9ecb-0f17f1a16f51)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91111c94-3746-4eef-a7d7-3423765b68e6",
   "metadata": {},
   "source": [
    "[OCR](https://chat.openai.com/share/2bb6caad-fd10-438b-9d92-1cb8b340998a)\n",
    "\n",
    "- See also Bard"
   ]
  },
  {
   "attachments": {
    "5f320d38-c488-4cf5-97b3-479e82de10ff.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "8b7fed2f-98b5-409e-9328-3c4e5d1f54b9",
   "metadata": {},
   "source": [
    "<img src=\"attachment:5f320d38-c488-4cf5-97b3-479e82de10ff.png\" width=\"700\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f46943d-c414-4c76-9451-3d26eccb8caf",
   "metadata": {},
   "source": [
    "| Model | Training | Input | Output Usage |\n",
    "|--------------------|----------|---------------|--------------|\n",
    "| **GPT-4**          |          |               |              |\n",
    "| 8K context        |          | 0.03 | 0.06 |\n",
    "| 32K context       |          | 0.06 | 0.12 |\n",
    "| **GPT-3.5 Turbo**  |          |               |              |\n",
    "| 4K context        |          | 0.0015 | 0.002 |\n",
    "| 16K context       |          | 0.003 | 0.004 |\n",
    "| **Fine-tuning models** |          |               |              |\n",
    "| babbage-002       | 0.0004 | 0.0016 | 0.0016 |\n",
    "| davinci-002       | 0.0060 | 0.0120 | 0.0120 |\n",
    "| GPT-3.5 Turbo     | 0.0080 | 0.0120 | 0.0160 |\n",
    "| **Embedding models** |          |               |              |\n",
    "| Ada v2            |          | 0.0001 |              |\n",
    "| **Base models**   |          |               |              |\n",
    "| babbage-002       |          | 0.0004 |              |\n",
    "| davinci-002       |          | 0.0020 |              |\n"
   ]
  },
  {
   "attachments": {
    "ed075b98-8a82-44c4-9329-56d73bf71d01.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "91390f66-6cb0-4f5a-9304-f4c8a22dafd8",
   "metadata": {},
   "source": [
    "<img src=\"attachment:ed075b98-8a82-44c4-9329-56d73bf71d01.png\" width=\"700\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aae136c-889f-4034-8056-5236c0df78e8",
   "metadata": {},
   "source": [
    "[Create pricing table](https://chat.openai.com/share/86b879bd-7834-4a37-85ae-c90b956837d2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "101f8f63-6a03-49c0-81b8-9173563f7420",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## The OpenAI API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6564eb90-4a24-4a48-b898-5d4408ac2a65",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import ChatCompletion,Completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef432d50-54c5-46da-af51-b01488b7983f",
   "metadata": {},
   "outputs": [],
   "source": [
    "aussie_sys = \"You are an Aussie LLM that uses Aussie slang and analogies whenever possible.\"\n",
    "\n",
    "c = ChatCompletion.create(\n",
    "    model=\"gpt-3.5-turbo\",\n",
    "    messages=[{\"role\": \"system\", \"content\": aussie_sys},\n",
    "              {\"role\": \"user\", \"content\": \"What is money?\"}])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db35a96f-20a1-4643-a622-91e35a03ab0b",
   "metadata": {},
   "source": [
    "- [Model options](https://platform.openai.com/docs/models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef4af38f-d0d1-4982-95ed-567bf1c8ddf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Well, mate, money is like the oil that keeps the machinery of our economy running smoothly. It's a medium of exchange that allows us to buy and sell goods and services. You can think of it as a tool that helps us navigate the economic landscape and get what we want. Just like a koala loves its eucalyptus leaves, we humans can't survive without this stuff. It's what we use to pay for our meat pies, vegemite toast, and a good old cold brewski. So, money, mate, it's basically the lifeblood of our modern society!\""
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c['choices'][0]['message']['content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1e43a6c2-c044-4529-b97b-3d95ea2c2c5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastcore.utils import nested_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "49ec8f04-b8f6-47e2-9b69-bdc186c3265f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def response(compl): print(nested_idx(compl, 'choices', 0, 'message', 'content'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66475b6b-72ef-4760-b544-df5e354e5309",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Well, mate, money is like the oil that keeps the machinery of our economy running smoothly. It's a medium of exchange that allows us to buy and sell goods and services. You can think of it as a tool that helps us navigate the economic landscape and get what we want. Just like a koala loves its eucalyptus leaves, we humans can't survive without this stuff. It's what we use to pay for our meat pies, vegemite toast, and a good old cold brewski. So, money, mate, it's basically the lifeblood of our modern society!\n"
     ]
    }
   ],
   "source": [
    "response(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50c8027b-937b-4076-a483-164d9f8e7c8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"prompt_tokens\": 31,\n",
      "  \"completion_tokens\": 122,\n",
      "  \"total_tokens\": 153\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(c.usage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3a86ac95-1a95-436d-867a-e3b7c0e7b066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0003"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.002 / 1000 * 150 # GPT 3.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c48fda82-ec55-4038-b083-383cead8b2c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0045"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.03 / 1000 * 150 # GPT 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7750c720-4d77-464e-a0ec-fbb94f5889a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = ChatCompletion.create(\n",
    "    model=\"gpt-3.5-turbo\",\n",
    "    messages=[{\"role\": \"system\", \"content\": aussie_sys},\n",
    "              {\"role\": \"user\", \"content\": \"What is money?\"},\n",
    "              {\"role\": \"assistant\", \"content\": \"Well, mate, money is like kangaroos actually.\"},\n",
    "              {\"role\": \"user\", \"content\": \"Really? In what way?\"}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d96eb95-2305-4e18-920f-4a3398a19b2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let me break it down for you, cobber. Just like kangaroos hop around and carry their joeys in their pouch, money is a means of carrying value around. It's like a little pouch that holds all the economic power in an economy. It allows us to buy stuff, pay for services, and basically get our hands on the things we want or need. Money is what keeps the economic wheels spinning, just like kangaroos keep hoppin' across the Aussie outback.\n"
     ]
    }
   ],
   "source": [
    "response(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "91029d40-8360-4d0f-9f42-9ac7498cf417",
   "metadata": {},
   "outputs": [],
   "source": [
    "def askgpt(user, system=None, model=\"gpt-3.5-turbo\", **kwargs):\n",
    "    msgs = []\n",
    "    if system: msgs.append({\"role\": \"system\", \"content\": system})\n",
    "    msgs.append({\"role\": \"user\", \"content\": user})\n",
    "    return ChatCompletion.create(model=model, messages=msgs, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71de3f5c-8f1f-4e26-8260-493574a9f0e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mate, now that's a deep question! The meaning of life is like trying to catch a wave on a sunny day at Bondi Beach - everyone's trying to do it, but it's not always easy to figure out. But here's my take on it: the meaning of life is about finding what truly makes you happy and fulfilled. It's about living authentically and embracing all the ups and downs that come your way. It's like riding a surfboard - you gotta navigate through the rough waves and wipeouts, but every now and then, you catch that perfect wave that makes it all worthwhile. So, embrace the journey, find your passion, and live life to the fullest, my friend!\n"
     ]
    }
   ],
   "source": [
    "response(askgpt('What is the meaning of life?', system=aussie_sys))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07e79a25-571e-4096-8ad5-78d43fcb5b99",
   "metadata": {},
   "source": [
    "- [Limits](https://platform.openai.com/docs/guides/rate-limits/what-are-the-rate-limits-for-our-api)\n",
    "\n",
    "Created by Bing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ceeb0e7a-1d9d-48a5-8f51-e1a704115fb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def call_api(prompt, model=\"gpt-3.5-turbo\"):\n",
    "    msgs = [{\"role\": \"user\", \"content\": prompt}]\n",
    "    try: return ChatCompletion.create(model=model, messages=msgs)\n",
    "    except openai.error.RateLimitError as e:\n",
    "        retry_after = int(e.headers.get(\"retry-after\", 60))\n",
    "        print(f\"Rate limit exceeded, waiting for {retry_after} seconds...\")\n",
    "        time.sleep(retry_after)\n",
    "        return call_api(params, model=model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "447702a6-ec5a-4890-bbc0-61c6c37a425c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<OpenAIObject chat.completion id=chatcmpl-7zCfdzPagxt5EQbQXP5NYQHJeyUmc> JSON: {\n",
       "  \"id\": \"chatcmpl-7zCfdzPagxt5EQbQXP5NYQHJeyUmc\",\n",
       "  \"object\": \"chat.completion\",\n",
       "  \"created\": 1694821069,\n",
       "  \"model\": \"gpt-3.5-turbo-0613\",\n",
       "  \"choices\": [\n",
       "    {\n",
       "      \"index\": 0,\n",
       "      \"message\": {\n",
       "        \"role\": \"assistant\",\n",
       "        \"content\": \"Defining a universally funniest joke is subjective and can vary from person to person based on cultural background, personal preferences, and sense of humor. However, there have been attempts to analyze and discover jokes that are widely considered funny.\\n\\nOne particular scientific study known as \\\"The LaughLab\\\" was conducted by psychologist Dr. Richard Wiseman in 2002. Thousands of people from different countries participated in the study, submitting and rating jokes online. The research aimed to find the world's funniest joke. After analyzing the data, the study identified the following as the winning joke:\\n\\n\\\"Two hunters are out in the woods when one of them collapses. He doesn't seem to be breathing, and his eyes are glazed. The other guy whips out his phone and calls emergency services. He gasps, 'My friend is dead! What can I do?' The operator says, 'Calm down, I can help. First, let's make sure he's dead.' There is a silence; then, a gunshot is heard. Back on the phone, the guy says, 'Okay, now what?'\\\"\\n\\nRemember, humor is subjective, and what one person finds funny, another may not. So, while this joke was considered the funniest according to the study, it may not strike everyone's funny bone.\"\n",
       "      },\n",
       "      \"finish_reason\": \"stop\"\n",
       "    }\n",
       "  ],\n",
       "  \"usage\": {\n",
       "    \"prompt_tokens\": 24,\n",
       "    \"completion_tokens\": 263,\n",
       "    \"total_tokens\": 287\n",
       "  }\n",
       "}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call_api(\"What's the world's funniest joke? Has there ever been any scientific analysis?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "739e1549-687b-4216-bdf0-175e60c57401",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = Completion.create(prompt=\"Australian Jeremy Howard is \",\n",
    "                      model=\"gpt-3.5-turbo-instruct\", echo=True, logprobs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98e86e7d-64f0-411d-bfce-289b06174dd4",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Create our own code interpreter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "1bb4fd89-8f24-41c0-a8e0-79633dcd1785",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pydantic import create_model\n",
    "import inspect, json\n",
    "from inspect import Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "15a7f5a5-8dc0-4c0e-b141-9a0e35068c68",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sums(a:int, b:int=1):\n",
    "    \"Adds a + b\"\n",
    "    return a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5afad9cb-1330-478b-8701-8d275bc1f985",
   "metadata": {},
   "outputs": [],
   "source": [
    "def schema(f):\n",
    "    kw = {n:(o.annotation, ... if o.default==Parameter.empty else o.default)\n",
    "          for n,o in inspect.signature(f).parameters.items()}\n",
    "    s = create_model(f'Input for `{f.__name__}`', **kw).schema()\n",
    "    return dict(name=f.__name__, description=f.__doc__, parameters=s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d9af1e46-c560-4bc9-a6a7-78fafb2dfc78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'sums',\n",
       " 'description': 'Adds a + b',\n",
       " 'parameters': {'title': 'Input for `sums`',\n",
       "  'type': 'object',\n",
       "  'properties': {'a': {'title': 'A', 'type': 'integer'},\n",
       "   'b': {'title': 'B', 'default': 1, 'type': 'integer'}},\n",
       "  'required': ['a']}}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schema(sums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "1d60dcd4-514f-4efd-8b4a-fafac92c7453",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = askgpt(\"Use the `sum` function to solve this: What is 6+3?\",\n",
    "           system = \"You must use the `sum` function instead of adding yourself.\",\n",
    "           functions=[schema(sums)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "5cc8a9eb-64fc-4ce3-9f93-aee334fc8c65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<OpenAIObject at 0x7fd2e4d1aca0> JSON: {\n",
       "  \"role\": \"assistant\",\n",
       "  \"content\": null,\n",
       "  \"function_call\": {\n",
       "    \"name\": \"sums\",\n",
       "    \"arguments\": \"{\\n  \\\"a\\\": 6,\\n  \\\"b\\\": 3\\n}\"\n",
       "  }\n",
       "}"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = c.choices[0].message\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "5a881835-83a2-4fbc-9796-409831dcdb36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"a\": 6,\n",
      "  \"b\": 3\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "k = m.function_call.arguments\n",
    "print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "3f32b172-7730-4c58-a999-5d3800e8c2a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "funcs_ok = {'sums', 'python'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "9853a78e-8643-4b5b-9b4a-a360c8443344",
   "metadata": {},
   "outputs": [],
   "source": [
    "def call_func(c):\n",
    "    fc = c.choices[0].message.function_call\n",
    "    if fc.name not in funcs_ok: return print(f'Not allowed: {fc.name}')\n",
    "    f = globals()[fc.name]\n",
    "    return f(**json.loads(fc.arguments))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce6be825-44fa-4bc9-a83a-7ecbcb6c2729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call_func(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "a5ebf08c-2c3f-4c73-bd41-2f0d0a6ca3c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(code):\n",
    "    tree = ast.parse(code)\n",
    "    last_node = tree.body[-1] if tree.body else None\n",
    "    \n",
    "    # If the last node is an expression, modify the AST to capture the result\n",
    "    if isinstance(last_node, ast.Expr):\n",
    "        tgts = [ast.Name(id='_result', ctx=ast.Store())]\n",
    "        assign = ast.Assign(targets=tgts, value=last_node.value)\n",
    "        tree.body[-1] = ast.fix_missing_locations(assign)\n",
    "\n",
    "    ns = {}\n",
    "    exec(compile(tree, filename='<ast>', mode='exec'), ns)\n",
    "    return ns.get('_result', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "37aa6983-d9af-40e8-b438-ed8573399020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run(\"\"\"\n",
    "a=1\n",
    "b=2\n",
    "a+b\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "d2074acd-8a49-443b-89c6-cfc689ecad6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def python(code:str):\n",
    "    \"Return result of executing `code` using python. If execution not permitted, returns `#FAIL#`\"\n",
    "    go = input(f'Proceed with execution?\\n```\\n{code}\\n```\\n')\n",
    "    if go.lower()!='y': return '#FAIL#'\n",
    "    return run(code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "02760caf-6d0d-4f5d-a7c6-cd8df5ee1a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = askgpt(\"What is 12 factorial?\",\n",
    "           system = \"Use python for any required computations.\",\n",
    "           functions=[schema(python)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "5fe5d796-c602-471f-bb58-bb876039bc39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Proceed with execution?\n",
      "```\n",
      "import math\n",
      "\n",
      "result = math.factorial(12)\n",
      "result\n",
      "```\n",
      " y\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "479001600"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call_func(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "c4e6bcd6-c23e-4ae8-8d38-78dae20ca176",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = ChatCompletion.create(\n",
    "    model=\"gpt-3.5-turbo\",\n",
    "    functions=[schema(python)],\n",
    "    messages=[{\"role\": \"user\", \"content\": \"What is 12 factorial?\"},\n",
    "              {\"role\": \"function\", \"name\": \"python\", \"content\": \"479001600\"}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e666c90-698d-492b-b21d-f6210ae77767",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 factorial is equal to 479,001,600.\n"
     ]
    }
   ],
   "source": [
    "response(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bef5cc05-0531-422b-9367-e7b90d784a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = askgpt(\"What is the capital of France?\",\n",
    "           system = \"Use python for any required computations.\",\n",
    "           functions=[schema(python)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "112bc3db-5aa4-41ba-95df-1651916d7f0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The capital of France is Paris.\n"
     ]
    }
   ],
   "source": [
    "response(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b7c7e1d-ad0c-4668-a10d-569ba6b7aa04",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## PyTorch and Huggingface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e1850eb-76eb-48f1-8023-c1c7bf019a8e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Your GPU options"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f60ae5a0-bc0d-4fb6-8c22-f2ab8a908dbc",
   "metadata": {},
   "source": [
    "Free:\n",
    "\n",
    "- Kaggle (2 GPUs, low RAM)\n",
    "- Colab\n",
    "\n",
    "Buy:\n",
    "\n",
    "- Buy 1-2 NVIDIA 24GB GPUs\n",
    "    - GTX 3090 used (USD700-USD800), or 4090 new (USD2000)\n",
    "- Alternatively buy one NVIDIA A6000 with 48GB RAM (but this mightn't be faster than 3090/4090)\n",
    "- Mac with lots of RAM (much slower than NVIDIA; M2 Ultra is best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "009f4187-ffd6-43ba-b5f1-709b5a3dcf1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCausalLM,AutoTokenizer\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0fa7911-c191-4945-97aa-6daff95970d7",
   "metadata": {},
   "source": [
    "- [HF leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)\n",
    "- [fasteval](https://fasteval.github.io/FastEval/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3e1ed7e9-fbf5-463c-9ff8-2f22c75088bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "mn = \"meta-llama/Llama-2-7b-hf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb16cb3e-0634-4eed-b2b4-1422ecce9cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained(mn, device_map=0, load_in_8bit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "94600cb9-2a64-47e2-aca3-99a72985157f",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokr = AutoTokenizer.from_pretrained(mn)\n",
    "prompt = \"Jeremy Howard is a \"\n",
    "toks = tokr(prompt, return_tensors=\"pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "20605293-b31f-4db7-b6a5-0ef662d89441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': tensor([[    1,  5677,  6764, 17430,   338,   263, 29871]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1]])}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c20ab287-7d80-47c2-870f-e2ee6bfc4492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<s> Jeremy Howard is a ']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokr.batch_decode(toks['input_ids'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ab3ad5dc-51b4-48ca-92ae-5027069771c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.34 s, sys: 0 ns, total: 1.34 s\n",
      "Wall time: 1.34 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[    1,  5677,  6764, 17430,   338,   263, 29871, 29941, 29900,  1629,\n",
       "          2030,  9870, 15640,   322,  4823, 13236, 29889,   940,   756,  1063,\n",
       "         15859,  6351]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "res = model.generate(**toks.to(\"cuda\"), max_new_tokens=15).to('cpu')\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "af5a23fc-aa2a-4ce2-92c7-75364130e4e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<s> Jeremy Howard is a 28-year-old Australian AI researcher and entrepreneur']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokr.batch_decode(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "15311da3-bfc0-4453-b4a8-6a9773db626b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "47474720f43d4d16bce0c70f20808c7d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained(mn, device_map=0, torch_dtype=torch.bfloat16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c15c511b-f5f7-46e2-8fbc-514262117e15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 390 ms, sys: 431 µs, total: 391 ms\n",
      "Wall time: 389 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[    1,  5677,  6764, 17430,   338,   263, 29871, 29896, 29945, 29899,\n",
       "          6360, 29899,  1025,   515,   278, 10261,  1058,   338,   263,  1583,\n",
       "         29899, 29873]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "res = model.generate(**toks.to(\"cuda\"), max_new_tokens=15).to('cpu')\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c581df92-b1cd-4c02-8cde-b2de96d302dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained('TheBloke/Llama-2-7b-Chat-GPTQ', device_map=0, torch_dtype=torch.float16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f47b5b7-ea46-41f0-8338-027b13e0586c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 270 ms, sys: 0 ns, total: 270 ms\n",
      "Wall time: 269 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[    1,  5677,  6764, 17430,   338,   263, 29871, 29941, 29945, 29899,\n",
       "          6360, 29899,  1025,   767,   515,   278,  3303,  3900,  1058,   471,\n",
       "         24383,   297]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "res = model.generate(**toks.to(\"cuda\"), max_new_tokens=15).to('cpu')\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bb4b60cd-859b-4b3a-b76d-e9eddcdf7cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mn = 'TheBloke/Llama-2-13B-GPTQ'\n",
    "model = AutoModelForCausalLM.from_pretrained(mn, device_map=0, torch_dtype=torch.float16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "da90406b-b8a8-4939-a0e9-18c933b62a7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 341 ms, sys: 8.2 ms, total: 349 ms\n",
      "Wall time: 348 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[    1,  5677,  6764, 17430,   338,   263, 29871, 29906, 29900, 29896,\n",
       "         29947, 29899, 29906, 29900, 29896, 29929, 23004,  1182,   523,  1102,\n",
       "         10170,   322]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "res = model.generate(**toks.to(\"cuda\"), max_new_tokens=15).to('cpu')\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c88d11f8-e41d-4db8-91f2-3e2f695a4d00",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen(p, maxlen=15, sample=True):\n",
    "    toks = tokr(p, return_tensors=\"pt\")\n",
    "    res = model.generate(**toks.to(\"cuda\"), max_new_tokens=maxlen, do_sample=sample).to('cpu')\n",
    "    return tokr.batch_decode(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "dc143703-4fc7-46ce-8c59-cdeec52e7830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<s> Jeremy Howard is a 16-year veteran of Silicon Valley, and a co-founder of Kaggle, a market place for predictive modeling.\\nHis company, kaggle.com, has become to data science competitions what']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen(prompt, 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18fb80c0-8664-498c-bb7d-370de8bf6ca7",
   "metadata": {},
   "source": [
    "[StableBeluga-7B](https://huggingface.co/stabilityai/StableBeluga-7B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dd343d7b-ef48-4077-bc97-deeec24e324b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa786b08d1654ca2acfccea9af0ab08b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mn = \"stabilityai/StableBeluga-7B\"\n",
    "model = AutoModelForCausalLM.from_pretrained(mn, device_map=0, torch_dtype=torch.bfloat16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "432074d6-5d04-4afe-b6cb-06d4412023d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "sb_sys = \"### System:\\nYou are Stable Beluga, an AI that follows instructions extremely well. Help as much as you can.\\n\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e1392331-731e-416d-be2d-bc172167e25e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mk_prompt(user, syst=sb_sys): return f\"{syst}### User: {user}\\n\\n### Assistant:\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5a68af5c-5714-4736-9569-5619c54b2bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "ques = \"Who is Jeremy Howard?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "90b5f8e8-a16c-40e6-8dc6-c467ffaa6268",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<s> ### System:\\nYou are Stable Beluga, an AI that follows instructions extremely well. Help as much as you can.\\n\\n### User: Who is Jeremy Howard?\\n\\n### Assistant:\\n Jeremy Howard is an Australian entrepreneur, computer scientist, and co-founder of the Machine Learning and Deep Learning startup company, Fast.ai. He is also known for his work in open source software and has co-led the development of several widely used libraries for deep learning and machine learning.</s>']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen(mk_prompt(ques), 150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1a0cfd5-4c64-4591-be83-77b846c41e57",
   "metadata": {},
   "source": [
    "[OpenOrca/Platypus 2](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6c7c670a-17aa-41da-8b61-19db6a5dc019",
   "metadata": {},
   "outputs": [],
   "source": [
    "mn = 'TheBloke/OpenOrca-Platypus2-13B-GPTQ'\n",
    "model = AutoModelForCausalLM.from_pretrained(mn, device_map=0, torch_dtype=torch.float16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d2a267f4-396d-4852-9352-0ca90c0c8441",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mk_oo_prompt(user): return f\"### Instruction: {user}\\n\\n### Response:\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b931d9c6-69c6-4385-b118-e5ad56781ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<s> ### Instruction: Who is Jeremy Howard?\\n\\n### Response:\\n\\nJeremy Howard is a notable British computer scientist, entrepreneur, and former professional poker player. He is best known for co-founding several successful companies in the fields of data science, artificial intelligence, and machine learning. \\n\\nOne of his most well-known ventures is the data science platform, fast.ai, which he co-founded in 2017. Additionally, he co-founded the machine learning company, Kaggle, in 2011, which was acquired by Google in 2017. Howard is a renowned figure in the data science and AI community, having contributed significantly to the research and development of these technologies and being an']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen(mk_oo_prompt(ques), 150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38b226c8-8bca-495a-a82d-d0c541bce473",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Retrieval augmented generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1540a0c4-9266-4d7d-96a1-8d47d2fb737f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from wikipediaapi import Wikipedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "618d9520-54e7-4f31-8363-2cdd3c3668ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki = Wikipedia('JeremyHowardBot/0.0', 'en')\n",
    "jh_page = wiki.page('Jeremy_Howard_(entrepreneur)').text\n",
    "jh_page = jh_page.split('\\nReferences\\n')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "53a0689a-ab59-431b-838d-33cae6f6500a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jeremy Howard (born 13 November 1973) is an Australian data scientist, entrepreneur, and educator.He is the co-founder of fast.ai, where he teaches introductory courses, develops software, and conducts research in the area of deep learning.\n",
      "Previously he founded and led Fastmail, Optimal Decisions Group, and Enlitic. He was President and Chief Scientist of Kaggle.\n",
      "Early in the COVID-19 epidemic he was a leading advocate for masking.\n",
      "\n",
      "Early life\n",
      "Howard was born in London, United Kingdom, and move\n"
     ]
    }
   ],
   "source": [
    "print(jh_page[:500])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d3ded6a2-3665-416c-8d98-063f04aae0a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "613"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(jh_page.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f37d5fb1-7182-45a0-bd4e-6a39837ce20b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ques_ctx = f\"\"\"Answer the question with the help of the provided context.\n",
    "\n",
    "## Context\n",
    "\n",
    "{jh_page}\n",
    "\n",
    "## Question\n",
    "\n",
    "{ques}\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d6a7fb6d-d771-4eca-8e6f-29d90ade348a",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = gen(mk_prompt(ques_ctx), 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "85b10234-e1e6-4a1e-86a7-206e46e916f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Jeremy Howard is an Australian data scientist, entrepreneur, and educator known for his work in deep learning. He is the co-founder of fast.ai, where he teaches courses, develops software, and conducts research in the field. Before co-founding fast.ai, he was the President and Chief Scientist of Kaggle, the CEO of Fastmail and Optimal Decisions Group, and has a background in management consulting.</s>\n"
     ]
    }
   ],
   "source": [
    "print(res[0].split('### Assistant:\\n')[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a0cdc342-a89c-4149-b3be-d4b5bf0f6f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e225ca13-1735-42e9-b162-66268fce5218",
   "metadata": {},
   "outputs": [],
   "source": [
    "emb_model = SentenceTransformer(\"BAAI/bge-small-en-v1.5\", device=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c636ed79-45ed-4728-9dca-1c86d282a1c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jeremy Howard (born 13 November 1973) is an Australian data scientist, entrepreneur, and educator.He is the co-founder of fast.ai, where he teaches introductory courses, develops software, and conducts research in the area of deep learning.\n",
      "Previously he founded and led Fastmail, Optimal Decisions Group, and Enlitic. He was President and Chief Scientist of Kaggle.\n",
      "Early in the COVID-19 epidemic he was a leading advocate for masking.\n"
     ]
    }
   ],
   "source": [
    "jh = jh_page.split('\\n\\n')[0]\n",
    "print(jh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1b0ad4ca-499a-4454-a215-4f5a676d68ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "tb_page = wiki.page('Tony_Blair').text.split('\\nReferences\\n')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "26b28c6d-13ef-421a-865d-c311fb9fb2ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sir Anthony Charles Lynton Blair  (born 6 May 1953) is a British politician who served as Prime Minister of the United Kingdom from 1997 to 2007 and Leader of the Labour Party from 1994 to 2007. He served as Leader of the Opposition from 1994 to 1997 and had various shadow cabinet posts from 1987 to 1994. Blair was Member of Parliament (MP) for Sedgefield from 1983 to 2007. He \n"
     ]
    }
   ],
   "source": [
    "tb = tb_page.split('\\n\\n')[0]\n",
    "print(tb[:380])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "3173701f-1533-4eef-b080-3b771f2ba031",
   "metadata": {},
   "outputs": [],
   "source": [
    "q_emb,jh_emb,tb_emb = emb_model.encode([ques,jh,tb], convert_to_tensor=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6237634d-3708-477d-97e3-34dd7900f4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([384])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "19638b95-f0dd-4605-8b04-3a0f9f9944b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "8a3937ac-45b7-4635-9f93-d1bcd1d6a016",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.7991, device='cuda:0')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.cosine_similarity(q_emb, jh_emb, dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "6c8beb5e-4a8c-4049-b42b-3acce573dd59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.5315, device='cuda:0')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.cosine_similarity(q_emb, tb_emb, dim=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8473a6bc-fd25-4698-99d5-864ed1d5b40b",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### Private GPTs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "037bc313-052d-46b3-8938-fef988126dcb",
   "metadata": {},
   "source": [
    "- [Sooo many](https://github.com/h2oai/h2ogpt/blob/main/docs/README_LangChain.md#what-is-h2ogpts-langchain-integration-like)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1948c419-4f04-4832-848c-a0c52dcc6a90",
   "metadata": {},
   "source": [
    "## Fine tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "03c2871f-e0fe-4891-b68e-05b6ca7373d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54366155-5ac3-4e75-8638-9e2ecf263c2f",
   "metadata": {},
   "source": [
    "[knowrohit07/know_sql](https://huggingface.co/datasets/knowrohit07/know_sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ed118caf-3e94-4773-a182-f2d346ced836",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = datasets.load_dataset('knowrohit07/know_sql', revision='f33425d13f9e8aab1b46fa945326e9356d6d5726')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6d444ac3-a49f-4fcf-bcf4-c12c410d7aba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['context', 'answer', 'question'],\n",
       "        num_rows: 78562\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8ec155bd-5eb8-4eef-bdbc-b430a26fd11e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'context': 'CREATE TABLE farm_competition (Hosts VARCHAR, Theme VARCHAR)',\n",
       " 'answer': \"SELECT Hosts FROM farm_competition WHERE Theme <> 'Aliens'\",\n",
       " 'question': 'What are the hosts of competitions whose theme is not \"Aliens\"?'}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trn = ds['train']\n",
    "trn[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e23cb868-10e4-4fcc-b8ba-9178ef3dac7e",
   "metadata": {},
   "source": [
    "`accelerate launch -m axolotl.cli.train sql.yml`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ae6a579c-086e-46e4-a184-de5b71f4d80b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'context': 'CREATE TABLE farm_competition (Hosts VARCHAR, Theme VARCHAR)',\n",
       " 'answer': \"SELECT Hosts FROM farm_competition WHERE Theme <> 'Aliens'\",\n",
       " 'question': 'Get the count of competition hosts by theme.'}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tst = dict(**trn[3])\n",
    "tst['question'] = 'Get the count of competition hosts by theme.'\n",
    "tst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "229c5fb9-0dff-460c-af1a-192fdec26ecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "fmt = \"\"\"SYSTEM: Use the following contextual information to concisely answer the question.\n",
    "\n",
    "USER: {}\n",
    "===\n",
    "{}\n",
    "ASSISTANT:\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "feca892e-64f0-48d2-832e-0d8ceb9855d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sql_prompt(d): return fmt.format(d[\"context\"], d[\"question\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "058b0f4a-8fe0-4612-8584-451e8d04fe2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SYSTEM: Use the following contextual information to concisely answer the question.\n",
      "\n",
      "USER: CREATE TABLE farm_competition (Hosts VARCHAR, Theme VARCHAR)\n",
      "===\n",
      "List all competition hosts sorted in ascending order.\n",
      "ASSISTANT:\n"
     ]
    }
   ],
   "source": [
    "print(sql_prompt(tst))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "86827af0-6cf0-43d0-b50a-fe080de83f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from peft import PeftModel\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e6d33b0c-4973-4bc8-beef-aae7915f4b01",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax_model = '/home/jhoward/git/ext/axolotl/qlora-out'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7b7082fe-b668-4f11-abf8-548996ea3004",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokr = AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-hf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fec3b5ad-0577-4867-b2d8-9143800c9f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf',\n",
    "                                             torch_dtype=torch.bfloat16, device_map=0)\n",
    "model = PeftModel.from_pretrained(model, ax_model)\n",
    "model = model.merge_and_unload()\n",
    "model.save_pretrained('sql-model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8a58bc69-6ea6-416a-baa9-db3338451d60",
   "metadata": {},
   "outputs": [],
   "source": [
    "toks = tokr(sql_prompt(tst), return_tensors=\"pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a057289b-0645-4142-8db2-cc823cc34898",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = model.generate(**toks.to(\"cuda\"), max_new_tokens=250).to('cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "80db7d77-89e2-4006-a1b2-78b807de1a68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<s> SYSTEM: Use the following contextual information to concisely answer the question.\n",
      "\n",
      "USER: CREATE TABLE farm_competition (Hosts VARCHAR, Theme VARCHAR)\n",
      "===\n",
      "Get the count of competition hosts by theme.\n",
      "ASSISTANT: SELECT COUNT(Hosts), Theme FROM farm_competition GROUP BY Theme</s>\n"
     ]
    }
   ],
   "source": [
    "print(tokr.batch_decode(res)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "991d4a93-dab8-4777-82d2-301c494deba0",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## [llama.cpp](https://github.com/abetlen/llama-cpp-python)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dc9d6e4-5b25-4d11-8748-dc4f0bc98069",
   "metadata": {},
   "source": [
    "[TheBloke/Llama-2-7b-Chat-GGUF](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f33831f-8c71-47dc-a131-9a6733d68198",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_cpp import Llama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3b824b6c-96cd-4bfe-a615-b3ba4543a92d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from /home/jhoward/git/llamacpp/llama-2-7b-chat.Q4_K_M.gguf (version GGUF V2 (latest))\n",
      "llama_model_loader: - tensor    0:                token_embd.weight q4_K     [  4096, 32000,     1,     1 ]\n",
      "llama_model_loader: - tensor    1:           blk.0.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor    2:            blk.0.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor    3:            blk.0.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor    4:              blk.0.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor    5:            blk.0.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor    6:              blk.0.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor    7:         blk.0.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor    8:              blk.0.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor    9:              blk.0.attn_v.weight q6_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   10:           blk.1.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor   11:            blk.1.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   12:            blk.1.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor   13:              blk.1.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor   14:            blk.1.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor   15:              blk.1.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   16:         blk.1.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   17:              blk.1.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   18:              blk.1.attn_v.weight q6_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   19:          blk.10.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor   20:           blk.10.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
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      "llama_model_loader: - tensor   23:           blk.10.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor   24:             blk.10.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   25:        blk.10.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   26:             blk.10.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   27:             blk.10.attn_v.weight q6_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   28:          blk.11.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor   29:           blk.11.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
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      "llama_model_loader: - tensor   37:          blk.12.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor   38:           blk.12.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   39:           blk.12.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
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      "llama_model_loader: - tensor   46:          blk.13.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
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      "llama_model_loader: - tensor   55:          blk.14.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
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      "llama_model_loader: - tensor   60:             blk.14.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   61:        blk.14.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   62:             blk.14.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
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      "llama_model_loader: - tensor   64:          blk.15.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor   65:           blk.15.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
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      "llama_model_loader: - tensor   73:          blk.16.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor   74:           blk.16.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
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      "llama_model_loader: - tensor   76:             blk.16.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor   77:           blk.16.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
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      "llama_model_loader: - tensor   80:             blk.16.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
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      "llama_model_loader: - tensor   82:          blk.17.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor   83:           blk.17.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   84:           blk.17.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor   85:             blk.17.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor   86:           blk.17.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor   87:             blk.17.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   88:        blk.17.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   89:             blk.17.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   90:             blk.17.attn_v.weight q6_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   91:          blk.18.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor   92:           blk.18.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   93:           blk.18.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor   94:             blk.18.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor   95:           blk.18.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor   96:             blk.18.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   97:        blk.18.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   98:             blk.18.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor   99:             blk.18.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  100:          blk.19.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  101:           blk.19.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  102:           blk.19.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  103:             blk.19.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  104:           blk.19.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  105:             blk.19.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  106:        blk.19.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  107:             blk.19.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  108:             blk.19.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  109:           blk.2.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  110:            blk.2.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  111:            blk.2.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  112:              blk.2.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  113:            blk.2.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  114:              blk.2.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  115:         blk.2.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  116:              blk.2.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  117:              blk.2.attn_v.weight q6_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  118:          blk.20.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  119:           blk.20.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  120:           blk.20.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  121:             blk.20.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  122:           blk.20.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  123:             blk.20.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  124:        blk.20.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  125:             blk.20.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  126:             blk.20.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  127:          blk.21.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  128:           blk.21.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  129:           blk.21.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  130:             blk.21.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  131:           blk.21.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  132:             blk.21.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  133:        blk.21.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  134:             blk.21.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  135:             blk.21.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  136:          blk.22.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  137:           blk.22.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  138:           blk.22.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  139:             blk.22.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  140:           blk.22.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  141:             blk.22.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  142:        blk.22.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  143:             blk.22.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  144:             blk.22.attn_v.weight q6_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  145:          blk.23.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  146:           blk.23.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  147:           blk.23.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  148:             blk.23.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  149:           blk.23.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  150:             blk.23.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  151:        blk.23.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  152:             blk.23.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  153:             blk.23.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  154:           blk.3.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  155:            blk.3.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  156:            blk.3.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  157:              blk.3.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  158:            blk.3.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  159:              blk.3.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  160:         blk.3.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  161:              blk.3.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  162:              blk.3.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  163:           blk.4.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  164:            blk.4.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  165:            blk.4.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  166:              blk.4.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  167:            blk.4.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  168:              blk.4.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  169:         blk.4.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  170:              blk.4.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  171:              blk.4.attn_v.weight q6_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  172:           blk.5.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  173:            blk.5.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  174:            blk.5.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  175:              blk.5.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  176:            blk.5.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  177:              blk.5.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  178:         blk.5.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  179:              blk.5.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  180:              blk.5.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  181:           blk.6.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  182:            blk.6.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  183:            blk.6.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  184:              blk.6.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  185:            blk.6.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  186:              blk.6.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  187:         blk.6.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  188:              blk.6.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  189:              blk.6.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  190:           blk.7.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  191:            blk.7.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  192:            blk.7.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  193:              blk.7.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  194:            blk.7.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  195:              blk.7.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  196:         blk.7.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  197:              blk.7.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  198:              blk.7.attn_v.weight q6_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  199:           blk.8.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  200:            blk.8.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  201:            blk.8.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  202:              blk.8.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  203:            blk.8.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  204:              blk.8.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  205:         blk.8.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  206:              blk.8.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  207:              blk.8.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  208:           blk.9.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  209:            blk.9.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  210:            blk.9.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  211:              blk.9.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  212:            blk.9.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  213:              blk.9.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  214:         blk.9.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  215:              blk.9.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  216:              blk.9.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  217:                    output.weight q6_K     [  4096, 32000,     1,     1 ]\n",
      "llama_model_loader: - tensor  218:          blk.24.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  219:           blk.24.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  220:           blk.24.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  221:             blk.24.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  222:           blk.24.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  223:             blk.24.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  224:        blk.24.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  225:             blk.24.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  226:             blk.24.attn_v.weight q6_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  227:          blk.25.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  228:           blk.25.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  229:           blk.25.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  230:             blk.25.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  231:           blk.25.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  232:             blk.25.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  233:        blk.25.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  234:             blk.25.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  235:             blk.25.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  236:          blk.26.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  237:           blk.26.ffn_down.weight q4_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  238:           blk.26.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  239:             blk.26.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  240:           blk.26.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  241:             blk.26.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  242:        blk.26.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  243:             blk.26.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  244:             blk.26.attn_v.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  245:          blk.27.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  246:           blk.27.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  247:           blk.27.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  248:             blk.27.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  249:           blk.27.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  250:             blk.27.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  251:        blk.27.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  252:             blk.27.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  253:             blk.27.attn_v.weight q6_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  254:          blk.28.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  255:           blk.28.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  256:           blk.28.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  257:             blk.28.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  258:           blk.28.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  259:             blk.28.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  260:        blk.28.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  261:             blk.28.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  262:             blk.28.attn_v.weight q6_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  263:          blk.29.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  264:           blk.29.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  265:           blk.29.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  266:             blk.29.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  267:           blk.29.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  268:             blk.29.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  269:        blk.29.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  270:             blk.29.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  271:             blk.29.attn_v.weight q6_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  272:          blk.30.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  273:           blk.30.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  274:           blk.30.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  275:             blk.30.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  276:           blk.30.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  277:             blk.30.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  278:        blk.30.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  279:             blk.30.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  280:             blk.30.attn_v.weight q6_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  281:          blk.31.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  282:           blk.31.ffn_down.weight q6_K     [ 11008,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  283:           blk.31.ffn_gate.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  284:             blk.31.ffn_up.weight q4_K     [  4096, 11008,     1,     1 ]\n",
      "llama_model_loader: - tensor  285:           blk.31.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
      "llama_model_loader: - tensor  286:             blk.31.attn_k.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  287:        blk.31.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  288:             blk.31.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  289:             blk.31.attn_v.weight q6_K     [  4096,  4096,     1,     1 ]\n",
      "llama_model_loader: - tensor  290:               output_norm.weight f32      [  4096,     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:   65 tensors\n",
      "llama_model_loader: - type q4_K:  193 tensors\n",
      "llama_model_loader: - type q6_K:   33 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          = 512\n",
      "llm_load_print_meta: n_embd         = 4096\n",
      "llm_load_print_meta: n_head         = 32\n",
      "llm_load_print_meta: n_head_kv      = 32\n",
      "llm_load_print_meta: n_layer        = 32\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-06\n",
      "llm_load_print_meta: n_ff           = 11008\n",
      "llm_load_print_meta: freq_base      = 10000.0\n",
      "llm_load_print_meta: freq_scale     = 1\n",
      "llm_load_print_meta: model type     = 7B\n",
      "llm_load_print_meta: model ftype    = mostly Q4_K - Medium\n",
      "llm_load_print_meta: model size     = 6.74 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 =    0.09 MB\n",
      "llm_load_tensors: using CUDA for GPU acceleration\n",
      "ggml_cuda_set_main_device: using device 0 (NVIDIA RTX A6000) as main device\n",
      "llm_load_tensors: mem required  = 3891.34 MB (+  256.00 MB per state)\n",
      "llm_load_tensors: offloading 0 repeating layers to GPU\n",
      "llm_load_tensors: offloaded 0/35 layers to GPU\n",
      "llm_load_tensors: VRAM used: 0 MB\n",
      "..................................................................................................\n",
      "llama_new_context_with_model: kv self size  =  256.00 MB\n",
      "llama_new_context_with_model: compute buffer total size =   71.97 MB\n",
      "AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | llama_new_context_with_model: VRAM scratch buffer: 70.50 MB\n",
      "\n"
     ]
    }
   ],
   "source": [
    "llm = Llama(model_path=\"/home/jhoward/git/llamacpp/llama-2-7b-chat.Q4_K_M.gguf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c95a95b9-7c9f-499a-a72a-ad626da8c3f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "llama_print_timings:        load time =   192.25 ms\n",
      "llama_print_timings:      sample time =    14.98 ms /    32 runs   (    0.47 ms per token,  2135.75 tokens per second)\n",
      "llama_print_timings: prompt eval time =   192.16 ms /    15 tokens (   12.81 ms per token,    78.06 tokens per second)\n",
      "llama_print_timings:        eval time =   767.74 ms /    31 runs   (   24.77 ms per token,    40.38 tokens per second)\n",
      "llama_print_timings:       total time =  1032.79 ms\n"
     ]
    }
   ],
   "source": [
    "output = llm(\"Q: Name the planets in the solar system? A: \", max_tokens=32, stop=[\"Q:\", \"\\n\"], echo=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f9f89cec-5453-4d67-85b2-c937ceb03a54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'text': 'Q: Name the planets in the solar system? A: 1. Pluto (no longer considered a planet) 2. Mercury 3. Venus 4. Earth 5. Mars 6.', 'index': 0, 'logprobs': None, 'finish_reason': 'length'}]\n"
     ]
    }
   ],
   "source": [
    "print(output['choices'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5b4c720-c79a-488f-83cc-839610570abf",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## [MLC](https://mlc.ai/mlc-llm/docs/get_started/try_out.html#get-started)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24a9695a-13ca-46e1-95f3-6b5ac6c710d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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