"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",
"# Evaluating Instruction Responses Locally Using a Llama 3 Model Via Ollama"
]
},
{
"cell_type": "markdown",
"id": "a128651b-f326-4232-a994-42f38b7ed520",
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"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)"
"- 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)"
"- For macOS and Windows users, click on the ollama application you downloaded; if it prompts you to install the command line usage, say \"yes\"\n",
"- Linux users can use the installation command provided on the ollama website\n",
"\n",
"- In general, before we can use ollama from the command line, we have to either start the ollama application or run `ollama serve` in a separate terminal\n",
"- With the ollama application or `ollama serve` running, in a different terminal, 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",
"- 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",
"1. Grasses: Llamas love to graze on various types of grasses, including tall grasses, short grasses, and even weeds.\n",
"2. Hay: High-quality hay, such as alfalfa or timothy hay, is a staple in a llama's diet. They enjoy the sweet taste and texture of fresh hay.\n",
"3. Grains: Llamas may receive grains like oats, barley, or corn as part of their daily ration. However, it's essential to provide these grains in moderation, as they can be high in calories.\n",
"4. Fruits and vegetables: Llamas enjoy a variety of fruits and veggies, such as apples, carrots, sweet potatoes, and leafy greens like kale or spinach.\n",
"5. Minerals: Llamas require access to mineral supplements, which help maintain their overall health and well-being.\n",
"In captivity, llama owners typically provide a balanced diet that includes a mix of hay, grains, and fruits/vegetables. It's essential to consult with a veterinarian or experienced llama breeder to determine the best feeding plan for your llama.\n"
"- 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",
"The correct answer is \"The hypotenuse of the triangle is 10 cm.\", not \"3 cm.\". The model failed to accurately calculate the length of the hypotenuse, which is a fundamental concept in geometry and trigonometry.\n",
"To me, this seems like a completely different question being asked. The original instruction was to rewrite the sentence in a more formal way, and the model response doesn't even attempt to do that. It's asking a new question altogether!\n",
"* The input sentence \"Did you finish the report?\" is indeed an interrogative sentence, which asks a question.\n",
"* The model response says it's exclamatory, which is incorrect. Exclamatory sentences are typically marked by an exclamation mark (!) and express strong emotions or emphasis, whereas this sentence is simply asking a question.\n",
"The correct output \"The type of sentence is interrogative.\" is the best possible score (100), while the model response is significantly off the mark, hence the low score.\n",
"- 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 an M3 MacBook Air laptop)\n",
"- Note that ollama is not fully deterministic across operating systems (as of this writing) so the numbers you are getting might slightly differ from the ones shown below"
