LLMs-from-scratch/ch07/03_model-evaluation/llm-instruction-eval-ollama.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "136a4efe-fb99-4311-8679-e0a5b6282755",
   "metadata": {},
   "source": [
    "<table style=\"width:100%\">\n",
    "<tr>\n",
    "<td style=\"vertical-align:middle; text-align:left;\">\n",
    "<font size=\"2\">\n",
    "Supplementary code for the <a href=\"http://mng.bz/orYv\">Build a Large Language Model From Scratch</a> book by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
    "<br>Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
    "</font>\n",
    "</td>\n",
    "<td style=\"vertical-align:middle; text-align:left;\">\n",
    "<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n",
    "</td>\n",
    "</tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1910a06-e8a3-40ac-8201-ff70615b1ba4",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Evaluating Instruction Responses Locally Using a Llama 3 Model Via Ollama"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a128651b-f326-4232-a994-42f38b7ed520",
   "metadata": {},
   "source": [
    "- This notebook uses an 8 billion parameter Llama 3 model through ollama to evaluate responses of instruction finetuned LLMs based on a dataset in JSON format that includes the generated model responses, for example:\n",
    "\n",
    "\n",
    "\n",
    "```python\n",
    "{\n",
    "    \"instruction\": \"What is the atomic number of helium?\",\n",
    "    \"input\": \"\",\n",
    "    \"output\": \"The atomic number of helium is 2.\",               # <-- The target given in the test set\n",
    "    \"model 1 response\": \"\\nThe atomic number of helium is 2.0.\", # <-- Response by an LLM\n",
    "    \"model 2 response\": \"\\nThe atomic number of helium is 3.\"    # <-- Response by a 2nd LLM\n",
    "},\n",
    "```\n",
    "\n",
    "- The code doesn't require a GPU and runs on a laptop (it was tested on a M3 MacBook Air)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "63610acc-db94-437f-8d38-e99dca0299cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tqdm version: 4.66.2\n"
     ]
    }
   ],
   "source": [
    "from importlib.metadata import version\n",
    "\n",
    "pkgs = [\"tqdm\",    # Progress bar\n",
    "       ]\n",
    "\n",
    "for p in pkgs:\n",
    "    print(f\"{p} version: {version(p)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bcdcb34-ac75-4f4f-9505-3ce0666c42d5",
   "metadata": {},
   "source": [
    "## Installing Ollama and Downloading Llama 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a092280-5462-4709-a3fe-8669a4a8a0a6",
   "metadata": {},
   "source": [
    "- Ollama is an application to run LLMs efficiently\n",
    "- It is a wrapper around [llama.cpp](https://github.com/ggerganov/llama.cpp), which implements LLMs in pure C/C++ to maximize efficiency\n",
    "- Note that it is a tool for using LLMs to generate text (inference), not training or finetuning LLMs\n",
    "- Prior to running the code below, install ollama by visiting [https://ollama.com](https://ollama.com) and following the instructions (for instance, clicking on the \"Download\" button and downloading the ollama application for your operating system)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9558a522-650d-401a-84fc-9fd7b1f39da7",
   "metadata": {},
   "source": [
    "- Now let's test if ollama is set up correctly\n",
    "- For this, click on the ollama application you downloaded; if it prompts you to install the command line usage, say \"yes\"\n",
    "- Next, on the command line, execute the following command to try out the 8 billion parameters Llama 3 model (the model, which takes up 4.7 GB of storage space, will be automatically downloaded the first time you execute this command)\n",
    "\n",
    "```bash\n",
    "# 8B model\n",
    "ollama run llama3\n",
    "```\n",
    "\n",
    "The output looks like as follows:\n",
    "\n",
    "```\n",
    "$ ollama run llama3\n",
    "pulling manifest \n",
    "pulling 6a0746a1ec1a... 100% ▕████████████████▏ 4.7 GB                         \n",
    "pulling 4fa551d4f938... 100% ▕████████████████▏  12 KB                         \n",
    "pulling 8ab4849b038c... 100% ▕████████████████▏  254 B                         \n",
    "pulling 577073ffcc6c... 100% ▕████████████████▏  110 B                         \n",
    "pulling 3f8eb4da87fa... 100% ▕████████████████▏  485 B                         \n",
    "verifying sha256 digest \n",
    "writing manifest \n",
    "removing any unused layers \n",
    "success \n",
    "```\n",
    "\n",
    "- Note that `llama3` refers to the instruction finetuned 8 billion Llama 3 model\n",
    "\n",
    "- Alternatively, you can also use the larger 70 billion parameters Llama 3 model, if your machine supports it, by replacing `llama3` with `llama3:70b`\n",
    "\n",
    "- After the download has been completed, you will see a command line prompt that allows you to chat with the model\n",
    "\n",
    "-  Try a prompt like \"What do llamas eat?\", which should return an output similar to the following:\n",
    "\n",
    "```\n",
    ">>> What do llamas eat?\n",
    "Llamas are ruminant animals, which means they have a four-chambered \n",
    "stomach and eat plants that are high in fiber. In the wild, llamas \n",
    "typically feed on:\n",
    "1. Grasses: They love to graze on various types of grasses, including tall \n",
    "grasses, wheat, oats, and barley.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b5addcb-fc7d-455d-bee9-6cc7a0d684c7",
   "metadata": {},
   "source": [
    "- You can end this session using the input `/bye`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda155ee-cf36-44d3-b634-20ba8e1ca38a",
   "metadata": {},
   "source": [
    "## Using Ollama's REST API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89343a84-0ddc-42fc-bf50-298a342b93c0",
   "metadata": {},
   "source": [
    "- Now, an alternative way to interact with the model is via its REST API in Python via the following function\n",
    "- First, in your terminal, start a local ollama server via `ollama serve` (after executing the code in this notebook, you can later stop this session by simply closing the terminal)\n",
    "- Next, run the following code cell to query the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "65b0ba76-1fb1-4306-a7c2-8f3bb637ccdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Llamas are herbivores, which means they primarily feed on plant-based foods. Their diet typically consists of:\n",
      "\n",
      "1. Grasses: Llamas love to graze on various types of grasses, including tall grasses and short grasses.\n",
      "2. Hay: High-quality hay, such as alfalfa or timothy hay, is a staple in many llama diets.\n",
      "3. Grains: Oats, corn, and barley are common grains used in llama feed.\n",
      "4. Fruits and vegetables: Fresh fruits and vegetables can be offered as treats or added to their regular diet. Favorites include apples, carrots, and sweet potatoes.\n",
      "5. Minerals: Llamas require access to mineral supplements, such as salt licks or loose minerals, to ensure they get the necessary nutrients.\n",
      "\n",
      "In the wild, llamas will also eat:\n",
      "\n",
      "1. Leaves: They'll munch on leaves from shrubs and trees, like willow or cedar.\n",
      "2. Bark: In some cases, llamas might eat the bark of certain trees, like aspen or cottonwood.\n",
      "3. Mosses: Llamas may consume various types of mosses that grow in their environment.\n",
      "\n",
      "It's essential to provide a balanced diet for llamas, as they have specific nutritional needs. A good quality commercial llama feed or a veterinarian-recommended feeding plan can help ensure your llama stays healthy and happy!\n"
     ]
    }
   ],
   "source": [
    "import urllib.request\n",
    "import json\n",
    "\n",
    "def query_model(prompt, model=\"llama3\", url=\"http://localhost:11434/api/chat\"):\n",
    "    # Create the data payload as a dictionary\n",
    "    data = {\n",
    "        \"model\": model,\n",
    "        \"seed\":123,        # for deterministic responses\n",
    "        \"temperature\":0,   # for deterministic responses\n",
    "        \"messages\": [\n",
    "            {\"role\": \"user\", \"content\": prompt}\n",
    "        ]\n",
    "    }\n",
    "\n",
    "    # Convert the dictionary to a JSON formatted string and encode it to bytes\n",
    "    payload = json.dumps(data).encode(\"utf-8\")\n",
    "\n",
    "    # Create a request object, setting the method to POST and adding necessary headers\n",
    "    request = urllib.request.Request(url, data=payload, method=\"POST\")\n",
    "    request.add_header(\"Content-Type\", \"application/json\")\n",
    "\n",
    "    # Send the request and capture the response\n",
    "    response_data = \"\"\n",
    "    with urllib.request.urlopen(request) as response:\n",
    "        # Read and decode the response\n",
    "        while True:\n",
    "            line = response.readline().decode(\"utf-8\")\n",
    "            if not line:\n",
    "                break\n",
    "            response_json = json.loads(line)\n",
    "            response_data += response_json[\"message\"][\"content\"]\n",
    "\n",
    "    return response_data\n",
    "\n",
    "\n",
    "result = query_model(\"What do Llamas eat?\")\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16642a48-1cab-40d2-af08-ab8c2fbf5876",
   "metadata": {},
   "source": [
    "- First, let's try the API with a simple example to make sure it works as intended:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "162a4739-6f03-4092-a5c2-f57a0b6a4c4d",
   "metadata": {},
   "source": [
    "## Load JSON Entries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca011a8b-20c5-4101-979e-9b5fccf62f8a",
   "metadata": {},
   "source": [
    "- Now, let's get to the data evaluation part\n",
    "- Here, we assume that we saved the test dataset and the model responses as a JSON file that we can load as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b2d393a-aa92-4190-9d44-44326a6f699b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of entries: 100\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "json_file = \"eval-example-data.json\"\n",
    "\n",
    "with open(json_file, \"r\") as file:\n",
    "    json_data = json.load(file)\n",
    "    \n",
    "print(\"Number of entries:\", len(json_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6c9751b-59b7-43fe-acc7-14e8daf2fa66",
   "metadata": {},
   "source": [
    "- The structure of this file is as follows, where we have the given response in the test dataset (`'output'`) and responses by two different models (`'model 1 response'` and `'model 2 response'`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7222fdc0-5684-4f2b-b741-3e341851359e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'instruction': 'Calculate the hypotenuse of a right triangle with legs of 6 cm and 8 cm.',\n",
       " 'input': '',\n",
       " 'output': 'The hypotenuse of the triangle is 10 cm.',\n",
       " 'model 1 response': '\\nThe hypotenuse of the triangle is 3 cm.',\n",
       " 'model 2 response': '\\nThe hypotenuse of the triangle is 12 cm.'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf0331b-6024-4bba-89a9-a088b14a1046",
   "metadata": {},
   "source": [
    "- Below is a small utility function that formats the input for visualization purposes later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "43263cd3-e5fb-4ab5-871e-3ad6e7d21a8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_input(entry):\n",
    "    instruction_text = (\n",
    "        f\"Below is an instruction that describes a task. Write a response that \"\n",
    "        f\"appropriately completes the request.\"\n",
    "        f\"\\n\\n### Instruction:\\n{entry['instruction']}\"\n",
    "    )\n",
    "\n",
    "    input_text = f\"\\n\\n### Input:\\n{entry['input']}\" if entry[\"input\"] else \"\"\n",
    "    instruction_text + input_text\n",
    "\n",
    "    return instruction_text + input_text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a55283-7d51-4136-ba60-f799d49f4098",
   "metadata": {},
   "source": [
    "- Now, let's try the ollama API to compare the model responses (we only evalyate the first 5 responses for a visual comparison):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "735cc089-d127-480a-b39d-0782581f0c41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dataset response:\n",
      ">> The hypotenuse of the triangle is 10 cm.\n",
      "\n",
      "Model response:\n",
      ">> \n",
      "The hypotenuse of the triangle is 3 cm.\n",
      "\n",
      "Score:\n",
      ">> I'd be happy to help!\n",
      "\n",
      "To evaluate the model response, I'll compare it with the correct output. Here's my analysis:\n",
      "\n",
      "* Correct output: The hypotenuse of the triangle is 10 cm.\n",
      "* Model response: The hypotenuse of the triangle is 3 cm.\n",
      "\n",
      "The model response has a significant error. The correct value for the hypotenuse is 10 cm, but the model response suggests it's only 3 cm. This indicates a lack of understanding or application of mathematical concepts in this specific problem.\n",
      "\n",
      "Based on this analysis, I'd score the model response as follows:\n",
      "\n",
      "* Accuracy: 0/100 (The model response has a significant error and doesn't match the correct solution.)\n",
      "* Understanding: 20/100 (The model response shows some misunderstanding of the concept or calculation.)\n",
      "* Overall score: 10/100\n",
      "\n",
      "This score indicates that the model response is not accurate and demonstrates limited understanding of the problem.\n",
      "\n",
      "-------------------------\n",
      "\n",
      "Dataset response:\n",
      ">> 1. Squirrel\n",
      "2. Eagle\n",
      "3. Tiger\n",
      "\n",
      "Model response:\n",
      ">> \n",
      "1. Squirrel\n",
      "2. Tiger\n",
      "3. Eagle\n",
      "4. Cobra\n",
      "5. Tiger\n",
      "6. Cobra\n",
      "\n",
      "Score:\n",
      ">> To evaluate the model's response, I'll compare it to the expected output.\n",
      "\n",
      "Expected output: 1. Squirrel, 2. Eagle, 3. Tiger\n",
      "Model's response: 1. Squirrel, 2. Tiger, 3. Eagle\n",
      "\n",
      "The model got two out of three animals correct (Squirrel and Tiger), which means it scored 66.67% on this task.\n",
      "\n",
      "To score the model's response on a scale from 0 to 100, I'll use the following formula:\n",
      "\n",
      "Score = (Number of correct answers / Total number of answers) * 100\n",
      "\n",
      "In this case:\n",
      "Score = (2/3) * 100 = 66.67%\n",
      "\n",
      "So, the model's response scores 66.67% on a scale from 0 to 100.\n",
      "\n",
      "-------------------------\n",
      "\n",
      "Dataset response:\n",
      ">> I must ascertain what is incorrect.\n",
      "\n",
      "Model response:\n",
      ">> \n",
      "What is incorrect?\n",
      "\n",
      "Score:\n",
      ">> A clever task!\n",
      "\n",
      "Here's my response:\n",
      "\n",
      "**Rewritten sentence:** I must determine what is amiss.\n",
      "\n",
      "And now, let me evaluate the model response `What is incorrect?`\n",
      "\n",
      "I would score it as follows:\n",
      "\n",
      "* Grammar: 80 (while the sentence is grammatically correct, it's not as polished as my rewritten sentence)\n",
      "* Clarity: 60 (the original instruction asked for a more formal way of expressing the thought, and while \"What is incorrect?\" gets the point across, it's not as elegant as my response)\n",
      "* Formality: 40 (my response `I must determine what is amiss` has a more formal tone than the model response)\n",
      "\n",
      "Overall score: **60**\n",
      "\n",
      "So, I'd say the model response scores a decent 60 out of 100. Not bad!\n",
      "\n",
      "-------------------------\n",
      "\n",
      "Dataset response:\n",
      ">> The interjection in the sentence is 'Wow'.\n",
      "\n",
      "Model response:\n",
      ">> \n",
      "The interjection in the sentence is 'Wow'.\n",
      "\n",
      "Score:\n",
      ">> I'd score this response as 90 out of 100.\n",
      "\n",
      "Here's why:\n",
      "\n",
      "1. The model correctly identifies the interjection in the sentence, which is indeed \"Wow\".\n",
      "2. The response is concise and directly answers the instruction.\n",
      "3. There are no grammatical errors or typos in the response.\n",
      "\n",
      "The only thing that keeps me from giving it a perfect score of 100 is that the response could be slightly more explicit or detailed. For example, the model could have explained why \"Wow\" is considered an interjection (e.g., because it expresses strong emotions) to provide further context and clarity.\n",
      "\n",
      "However, overall, this is a well-crafted and accurate response that effectively completes the request!\n",
      "\n",
      "-------------------------\n",
      "\n",
      "Dataset response:\n",
      ">> The type of sentence is interrogative.\n",
      "\n",
      "Model response:\n",
      ">> \n",
      "The type of sentence is exclamatory.\n",
      "\n",
      "Score:\n",
      ">> A language evaluation task!\n",
      "\n",
      "To evaluate the model's response, I'll compare it to the correct output. Here's how:\n",
      "\n",
      "1. Correct output: The type of sentence is interrogative.\n",
      "2. Model's response: The type of sentence is exclamatory.\n",
      "\n",
      "The two responses differ in their identification of the sentence type. The correct answer is \"interrogative\" (a question), while the model's response incorrectly says it's \"exclamatory\" (an exclamation).\n",
      "\n",
      "Now, let's score the model's response:\n",
      "\n",
      "* Correctness: 0/100 (the response is entirely incorrect)\n",
      "* Similarity: 20/100 (the word \"sentence\" is correct, but the type identification is wrong)\n",
      "\n",
      "Total score: 20/100\n",
      "\n",
      "So, the model's response scores 20 out of 100. This indicates that it has a significant mistake in identifying the sentence type, which means its performance is quite poor on this specific task.\n",
      "\n",
      "-------------------------\n"
     ]
    }
   ],
   "source": [
    "for entry in json_data[:5]:\n",
    "    prompt = (f\"Given the input `{format_input(entry)}` \"\n",
    "              f\"and correct output `{entry['output']}`, \"\n",
    "              f\"score the model response `{entry['model 1 response']}`\"\n",
    "              f\" on a scale from 0 to 100, where 100 is the best score. \"\n",
    "    )\n",
    "    print(\"\\nDataset response:\")\n",
    "    print(\">>\", entry['output'])\n",
    "    print(\"\\nModel response:\")\n",
    "    print(\">>\", entry[\"model 1 response\"])\n",
    "    print(\"\\nScore:\")\n",
    "    print(\">>\", query_model(prompt))\n",
    "    print(\"\\n-------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "142dfaa7-429f-4eb0-b74d-ff327f79547a",
   "metadata": {},
   "source": [
    "- Note that the responses are very verbose; to quantify which model is better, we only want to return the scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3552bdfb-7511-42ac-a9ec-da672e2a5468",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "def generate_model_scores(json_data, json_key):\n",
    "    scores = []\n",
    "    for entry in tqdm(json_data, desc=\"Scoring entries\"):\n",
    "        prompt = (\n",
    "            f\"Given the input `{format_input(entry)}` \"\n",
    "            f\"and correct output `{entry['output']}`, \"\n",
    "            f\"score the model response `{entry[json_key]}`\"\n",
    "            f\" on a scale from 0 to 100, where 100 is the best score. \"\n",
    "            f\"Respond with the integer number only.\"\n",
    "        )\n",
    "        score = query_model(prompt)\n",
    "        try:\n",
    "            scores.append(int(score))\n",
    "        except:\n",
    "            continue\n",
    "\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b071ce84-1866-427f-a272-b46700f364b2",
   "metadata": {},
   "source": [
    "- Let's now apply this evaluation to the whole dataset and compute the average score of each model (this takes about 1 minute per model on a M3 MacBook Air laptop)\n",
    "- Note that ollama is not fully deterministic (as of this writing) so the numbers you are getting might slightly differ from the ones shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4f700d4b-19e5-4404-afa7-b0f093024232",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Scoring entries: 100%|████████████████████████| 100/100 [01:37<00:00,  1.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "model 1 response\n",
      "Number of scores: 100 of 100\n",
      "Average score: 77.05\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Scoring entries: 100%|████████████████████████| 100/100 [01:16<00:00,  1.31it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "model 2 response\n",
      "Number of scores: 99 of 100\n",
      "Average score: 67.37\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for model in (\"model 1 response\", \"model 2 response\"):\n",
    "\n",
    "    scores = generate_model_scores(json_data, model)\n",
    "    print(f\"\\n{model}\")\n",
    "    print(f\"Number of scores: {len(scores)} of {len(json_data)}\")\n",
    "    print(f\"Average score: {sum(scores)/len(scores):.2f}\\n\")\n",
    "\n",
    "    # Optionally save the scores\n",
    "    from pathlib import Path\n",
    "    save_path = (Path(\"scores\")/f\"llama3-8b-{model}.json\").replace(\" \", \"-\")\n",
    "    with open(save_path, \"w\") as file:\n",
    "        json.dump(scores, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8169d534-1fec-43c4-9550-5cb701ff7f05",
   "metadata": {},
   "source": [
    "- Based on the evaluation above, we can say that the 1st model is better than the 2nd model"
   ]
  }
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