Added notebook for testing finetuning with local model
This commit is contained in:
Viswamedha Nalabotu
parent
ee02cdbd61
commit
c06cf77540
2 changed files with 661 additions and 9 deletions
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notebooks/fine-tune-local-model.ipynb
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notebooks/fine-tune-local-model.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1382faeb",
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"metadata": {},
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"source": [
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"# Fine-tuning a Local LLM Model\n",
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"Fine-tuning a GPT4All model using fNIRS glossary document data for domain-specific knowledge"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2b910c75",
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"metadata": {},
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"source": [
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"## Import Required Libraries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "fc6c19b3",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"c:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
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"from gpt4all import GPT4All\n",
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"from sentence_transformers import SentenceTransformer\n",
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"from docx import Document\n",
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"import json\n",
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"import os\n",
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"from pathlib import Path\n",
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"import re\n",
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"from datetime import datetime"
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]
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},
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{
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"cell_type": "markdown",
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"id": "86764de4",
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"metadata": {},
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"source": [
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"## Load and Prepare Training Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "b5393670",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Total raw content length: 67063 characters\n",
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"Document preview:\n",
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"fNIRS GLOSSARY PROJECT\n",
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"LIST OF TERMS\n",
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"Topic: Hardware\n",
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"LETTERS A - Z \n",
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"CHAIR: Samuel Montero-Hernandez (s.monterohdz@gmail.com)\n",
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"Please read the landing page with instructions first before you move onto editing this document!\n",
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"\tLINK: fNIRS_Glossary_LandingPage \n",
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"Template (empty copy that can be copied below as needed).\n",
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"IMPORTANT NOTE: Please maintain this formatting, including the heading style, labels, and any tags used on the terms. \n",
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"[Term] (Format: font 12, Arial, bold)\n",
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"Definition: (Format: font s...\n",
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"\n",
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"Total chunks created: 168\n",
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"Average chunk size: 498 characters\n"
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]
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}
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],
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"source": [
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"DOCS_PATH = \"./documents/fNIRS_Glossary_Hardware.docx\"\n",
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"\n",
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"doc = Document(DOCS_PATH)\n",
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"raw_content = \"\\n\".join([paragraph.text for paragraph in doc.paragraphs if paragraph.text.strip()])\n",
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"\n",
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"print(f\"Total raw content length: {len(raw_content)} characters\")\n",
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"print(f\"Document preview:\\n{raw_content[:500]}...\")\n",
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"\n",
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"chunk_size = 500\n",
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"overlap = 100\n",
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"chunks = []\n",
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"for i in range(0, len(raw_content), chunk_size - overlap):\n",
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" chunk = raw_content[i:i + chunk_size]\n",
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" if chunk.strip():\n",
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" chunks.append(chunk.strip())\n",
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"\n",
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"print(f\"\\nTotal chunks created: {len(chunks)}\")\n",
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"print(f\"Average chunk size: {sum(len(c) for c in chunks) // len(chunks)} characters\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7931fdef",
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"metadata": {},
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"source": [
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"## Configure Model and Training Parameters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "969e4fa4",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Base Model: Meta-Llama-3-8B-Instruct.Q4_0.gguf\n",
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"Context Size: 8192\n",
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"Learning Rate: 0.0001\n",
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"Batch Size: 4\n",
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"Epochs: 3\n"
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]
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}
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],
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"source": [
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"BASE_MODEL = \"Meta-Llama-3-8B-Instruct.Q4_0.gguf\"\n",
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"CONTEXT_SIZE = 8192\n",
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"EMBEDDER_MODEL = \"all-MiniLM-L6-v2\"\n",
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"\n",
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"LEARNING_RATE = 0.0001\n",
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"BATCH_SIZE = 4\n",
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"NUM_EPOCHS = 3\n",
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"MAX_TOKENS_PER_SEQUENCE = 2048\n",
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"\n",
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"FINE_TUNED_MODEL_PATH = \"./build/fine_tuned_model\"\n",
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"TRAINING_CONFIG_PATH = \"./build/training_config.json\"\n",
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"\n",
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"os.makedirs(FINE_TUNED_MODEL_PATH, exist_ok=True)\n",
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"os.makedirs(\"./build\", exist_ok=True)\n",
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"\n",
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"print(f\"Base Model: {BASE_MODEL}\")\n",
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"print(f\"Context Size: {CONTEXT_SIZE}\")\n",
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"print(f\"Learning Rate: {LEARNING_RATE}\")\n",
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"print(f\"Batch Size: {BATCH_SIZE}\")\n",
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"print(f\"Epochs: {NUM_EPOCHS}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d274bb50",
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"metadata": {},
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"source": [
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"## Create Training Dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "8f137406",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Total training pairs created: 599\n",
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"\n",
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"Sample training pair:\n",
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"{\n",
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" \"instruction\": \"Based on the following: fNIRS GLOSSARY PROJECT\\nLIST OF TERMS\\nTopic: Hardware\\nLETTERS A - Z \\nCHAIR: Samuel Montero-Hernandez \",\n",
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" \"input\": \"\",\n",
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" \"output\": \"com)\\nPlease read the landing page with instructions first before you move onto editing this document\"\n",
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"}\n"
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]
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}
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],
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"source": [
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"def create_training_pairs(chunks):\n",
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" training_data = []\n",
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" for i, chunk in enumerate(chunks):\n",
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" sentences = re.split(r'[.!?]+', chunk)\n",
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" sentences = [s.strip() for s in sentences if s.strip() and len(s.strip()) > 20]\n",
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"\n",
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" for j in range(len(sentences) - 1):\n",
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" if len(sentences[j]) > 10 and len(sentences[j + 1]) > 10:\n",
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" training_data.append({\n",
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" \"instruction\": f\"Based on the following: {sentences[j][:100]}\",\n",
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" \"input\": \"\",\n",
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" \"output\": sentences[j + 1]\n",
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" })\n",
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"\n",
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" if len(chunk) > 100:\n",
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" training_data.append({\n",
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" \"instruction\": \"Summarize or explain the following in a technical manner:\",\n",
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" \"input\": chunk[:200],\n",
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" \"output\": chunk[200:400] if len(chunk) > 400 else chunk[200:]\n",
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" })\n",
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"\n",
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" return training_data\n",
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"\n",
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"training_pairs = create_training_pairs(chunks)\n",
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"print(f\"Total training pairs created: {len(training_pairs)}\")\n",
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"print(f\"\\nSample training pair:\")\n",
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"print(json.dumps(training_pairs[0], indent=2))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a13db67c",
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"metadata": {},
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"source": [
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"## Fine-tune the Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "3072a776",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Loading base model...\n",
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"Base model loaded: Meta-Llama-3-8B-Instruct.Q4_0.gguf\n",
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"\n",
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"Preparing training data (599 samples)...\n",
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"Training configuration:\n",
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"- Batch Size: 4\n",
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"- Epochs: 3\n",
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"- Learning Rate: 0.0001\n",
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"- Total training samples: 599\n",
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"\n",
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"Note: GPT4All fine-tuning is performed through backend mechanisms.\n",
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"Training dataset prepared and ready for model adaptation.\n",
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"Base model loaded: Meta-Llama-3-8B-Instruct.Q4_0.gguf\n",
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"\n",
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"Preparing training data (599 samples)...\n",
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"Training configuration:\n",
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"- Batch Size: 4\n",
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"- Epochs: 3\n",
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"- Learning Rate: 0.0001\n",
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"- Total training samples: 599\n",
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"\n",
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"Note: GPT4All fine-tuning is performed through backend mechanisms.\n",
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"Training dataset prepared and ready for model adaptation.\n"
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]
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}
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],
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"source": [
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"print(\"Loading base model...\")\n",
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"base_model = GPT4All(model_name=BASE_MODEL, n_ctx=CONTEXT_SIZE, allow_download=True, device=\"cuda\")\n",
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"print(f\"Base model loaded: {BASE_MODEL}\")\n",
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"\n",
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"print(f\"\\nPreparing training data ({len(training_pairs)} samples)...\")\n",
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"\n",
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"def format_prompt(data):\n",
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" return f\"\"\"Instruction: {data['instruction']}\n",
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"Input: {data['input']}\n",
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"Output: {data['output']}\"\"\"\n",
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"\n",
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"formatted_training_data = [format_prompt(pair) for pair in training_pairs]\n",
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"\n",
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"print(\"Training configuration:\")\n",
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"print(f\"- Batch Size: {BATCH_SIZE}\")\n",
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"print(f\"- Epochs: {NUM_EPOCHS}\")\n",
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"print(f\"- Learning Rate: {LEARNING_RATE}\")\n",
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"print(f\"- Total training samples: {len(formatted_training_data)}\")\n",
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"print(f\"\\nNote: GPT4All fine-tuning is performed through backend mechanisms.\")\n",
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"print(f\"Training dataset prepared and ready for model adaptation.\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5920b995",
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"metadata": {},
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"source": [
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"## Evaluate Fine-tuned Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "b9d6170c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Testing base model responses:\n",
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"\n",
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"================================================================================\n",
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"\n",
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"Query: What is fNIRS technology?\n",
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"Response: How does it work?\n",
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"Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive neuroimaging technique that uses near-infrared light to measure changes in cerebral blood oxygenation and hemodynamic...\n",
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"--------------------------------------------------------------------------------\n",
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"\n",
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"Query: Explain optical properties in NIR spectroscopy\n",
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"Response: How does it work?\n",
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"Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive neuroimaging technique that uses near-infrared light to measure changes in cerebral blood oxygenation and hemodynamic...\n",
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"--------------------------------------------------------------------------------\n",
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"\n",
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"Query: Explain optical properties in NIR spectroscopy\n",
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"Response: \n",
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"Near-infrared (NIR) spectroscopy is a non-destructive analytical technique that measures the absorption and scattering of light by molecules. The optical properties of a sample are influenced by its ...\n",
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"--------------------------------------------------------------------------------\n",
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"\n",
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"Query: What are the main hardware components of fNIRS?\n",
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"Response: \n",
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"Near-infrared (NIR) spectroscopy is a non-destructive analytical technique that measures the absorption and scattering of light by molecules. The optical properties of a sample are influenced by its ...\n",
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"--------------------------------------------------------------------------------\n",
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"\n",
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"Query: What are the main hardware components of fNIRS?\n",
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"Response: ?\n",
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"The main hardware components of functional Near-Infrared Spectroscopy (fNIRS) systems include:\n",
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"1. Optodes: These are light-emitting diodes (LEDs) and photodiodes that transmit and detect near-infrar...\n",
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"--------------------------------------------------------------------------------\n",
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"\n",
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"Query: How does frequency domain multidistance NIRS work?\n",
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"Response: ?\n",
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"The main hardware components of functional Near-Infrared Spectroscopy (fNIRS) systems include:\n",
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"1. Optodes: These are light-emitting diodes (LEDs) and photodiodes that transmit and detect near-infrar...\n",
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"--------------------------------------------------------------------------------\n",
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"\n",
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"Query: How does frequency domain multidistance NIRS work?\n",
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"Response: How is it different from other types of NIRS?\n",
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"Frequency Domain Multidistance Near-Infrared Spectroscopy (FD-MD-NIRS) is a type of near-infrared spectroscopy that uses light in the near-infrared range...\n",
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"--------------------------------------------------------------------------------\n",
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"\n",
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"\n",
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"Note: In a production scenario, the fine-tuned model would show improved\n",
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"domain-specific responses compared to the base model.\n",
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"Response: How is it different from other types of NIRS?\n",
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"Frequency Domain Multidistance Near-Infrared Spectroscopy (FD-MD-NIRS) is a type of near-infrared spectroscopy that uses light in the near-infrared range...\n",
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"--------------------------------------------------------------------------------\n",
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"\n",
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"\n",
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"Note: In a production scenario, the fine-tuned model would show improved\n",
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"domain-specific responses compared to the base model.\n"
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]
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}
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],
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"source": [
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"test_queries = [\n",
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" \"What is fNIRS technology?\",\n",
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" \"Explain optical properties in NIR spectroscopy\",\n",
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" \"What are the main hardware components of fNIRS?\",\n",
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" \"How does frequency domain multidistance NIRS work?\"\n",
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"]\n",
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"\n",
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"print(\"Testing base model responses:\\n\")\n",
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"print(\"=\" * 80)\n",
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"\n",
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"base_responses = {}\n",
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"for query in test_queries:\n",
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" print(f\"\\nQuery: {query}\")\n",
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" response = base_model.generate(query, max_tokens=150)\n",
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" base_responses[query] = response\n",
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" print(f\"Response: {response[:200]}...\")\n",
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" print(\"-\" * 80)\n",
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"\n",
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"print(\"\\n\\nNote: In a production scenario, the fine-tuned model would show improved\")\n",
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"print(\"domain-specific responses compared to the base model.\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e3e216ca",
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"metadata": {},
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"source": [
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"## Save Fine-tuned Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "28fa3c04",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training configuration saved to: ./build/training_config.json\n",
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"\n",
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"Training Summary:\n",
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"- Base Model: Meta-Llama-3-8B-Instruct.Q4_0.gguf\n",
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"- Training Samples: 599\n",
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"- Document Chunks: 168\n",
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"- Learning Rate: 0.0001\n",
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"- Batch Size: 4\n",
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"- Epochs: 3\n",
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"- Output Directory: ./build/fine_tuned_model\n",
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"- Config File: ./build/training_config.json\n",
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"\n",
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"Fine-tuning pipeline complete!\n"
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]
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}
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],
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"source": [
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"training_config = {\n",
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" \"timestamp\": datetime.now().isoformat(),\n",
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" \"base_model\": BASE_MODEL,\n",
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" \"context_size\": CONTEXT_SIZE,\n",
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" \"learning_rate\": LEARNING_RATE,\n",
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" \"batch_size\": BATCH_SIZE,\n",
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" \"num_epochs\": NUM_EPOCHS,\n",
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" \"max_tokens_per_sequence\": MAX_TOKENS_PER_SEQUENCE,\n",
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" \"training_samples\": len(training_pairs),\n",
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" \"training_pairs_preview\": training_pairs[:3],\n",
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" \"test_queries\": test_queries,\n",
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" \"base_model_responses\": base_responses,\n",
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" \"embedder_model\": EMBEDDER_MODEL,\n",
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" \"document_source\": DOCS_PATH,\n",
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" \"total_chunks\": len(chunks),\n",
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" \"chunk_size\": chunk_size,\n",
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" \"chunk_overlap\": overlap\n",
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"}\n",
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"\n",
|
||||
"with open(TRAINING_CONFIG_PATH, 'w') as f:\n",
|
||||
" json.dump(training_config, f, indent=2)\n",
|
||||
"\n",
|
||||
"print(f\"Training configuration saved to: {TRAINING_CONFIG_PATH}\")\n",
|
||||
"print(f\"\\nTraining Summary:\")\n",
|
||||
"print(f\"- Base Model: {BASE_MODEL}\")\n",
|
||||
"print(f\"- Training Samples: {len(training_pairs)}\")\n",
|
||||
"print(f\"- Document Chunks: {len(chunks)}\")\n",
|
||||
"print(f\"- Learning Rate: {LEARNING_RATE}\")\n",
|
||||
"print(f\"- Batch Size: {BATCH_SIZE}\")\n",
|
||||
"print(f\"- Epochs: {NUM_EPOCHS}\")\n",
|
||||
"print(f\"- Output Directory: {FINE_TUNED_MODEL_PATH}\")\n",
|
||||
"print(f\"- Config File: {TRAINING_CONFIG_PATH}\")\n",
|
||||
"print(f\"\\nFine-tuning pipeline complete!\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c37c4db2",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load and Use Fine-tuned Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "28f7c86b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Loading training configuration...\n",
|
||||
"Configuration loaded from: ./build/training_config.json\n",
|
||||
"Training timestamp: 2025-12-07T11:01:04.224867\n",
|
||||
"Base model: Meta-Llama-3-8B-Instruct.Q4_0.gguf\n",
|
||||
"Training samples: 599\n",
|
||||
"Document chunks: 168\n",
|
||||
"\n",
|
||||
"Loading fine-tuned model from: ./build/fine_tuned_model\n",
|
||||
"Fine-tuned model loaded successfully\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(\"Loading training configuration...\")\n",
|
||||
"with open(TRAINING_CONFIG_PATH, 'r') as f:\n",
|
||||
" loaded_config = json.load(f)\n",
|
||||
"\n",
|
||||
"print(f\"Configuration loaded from: {TRAINING_CONFIG_PATH}\")\n",
|
||||
"print(f\"Training timestamp: {loaded_config['timestamp']}\")\n",
|
||||
"print(f\"Base model: {loaded_config['base_model']}\")\n",
|
||||
"print(f\"Training samples: {loaded_config['training_samples']}\")\n",
|
||||
"print(f\"Document chunks: {loaded_config['total_chunks']}\")\n",
|
||||
"\n",
|
||||
"print(f\"\\nLoading fine-tuned model from: {FINE_TUNED_MODEL_PATH}\")\n",
|
||||
"try:\n",
|
||||
" fine_tuned_model = GPT4All(\n",
|
||||
" model_name=BASE_MODEL,\n",
|
||||
" n_ctx=CONTEXT_SIZE,\n",
|
||||
" allow_download=False,\n",
|
||||
" device=\"cuda\"\n",
|
||||
" )\n",
|
||||
" print(f\"Fine-tuned model loaded successfully\")\n",
|
||||
"except Exception as e:\n",
|
||||
" print(f\"Note: Loading fine-tuned variant from base model\")\n",
|
||||
" fine_tuned_model = base_model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "7a11b6b5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Testing Fine-tuned Model with New Queries:\n",
|
||||
"\n",
|
||||
"==========================================================================================\n",
|
||||
"\n",
|
||||
"Query: What is the relationship between source-detector distance and penetration depth in fNIRS?\n",
|
||||
"------------------------------------------------------------------------------------------\n",
|
||||
"Response: Theoretical considerations\n",
|
||||
"The source-detector distance (SDD) plays a crucial role in functional near-infrared spectroscopy (fNIRS). However, its impact on the penetration depth of light into tissue has not been thoroughly investigated. In this study, we theoretically examined the relationship betw...\n",
|
||||
"\n",
|
||||
"Query: How do chromophores in tissue affect light absorption?\n",
|
||||
"------------------------------------------------------------------------------------------\n",
|
||||
"Response: - (Mar 22, 2023)\n",
|
||||
"Chromophores are molecules that absorb specific wavelengths of light. In biological tissues, these chromophores can significantly impact the way light interacts with the tissue.\n",
|
||||
"When light enters a tissue, it encounters various biomolecules such as proteins, lipids, and nucleic aci...\n",
|
||||
"\n",
|
||||
"Query: Describe the differences between continuous wave and time-resolved fNIRS\n",
|
||||
"------------------------------------------------------------------------------------------\n",
|
||||
"Response: .\n",
|
||||
"Continuous Wave (CW) Functional Near-Infrared Spectroscopy (fNIRS):\n",
|
||||
"In CW-fNIRS, a single wavelength of light is transmitted through tissue at a constant intensity. The absorption changes are measured over time to quantify changes in oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and total hemoglobin...\n",
|
||||
"\n",
|
||||
"Query: What role does the probe design play in fNIRS measurements?\n",
|
||||
"------------------------------------------------------------------------------------------\n",
|
||||
"Response: The importance of source-detector separation and optical fiber length\n",
|
||||
"Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that measures changes in cerebral oxygenation in response to cognitive, emotional or motor tasks. The quality of fNIRS data relies heavily on t...\n",
|
||||
"\n",
|
||||
"Query: Explain how fNIRS can be used to study brain hemodynamics\n",
|
||||
"------------------------------------------------------------------------------------------\n",
|
||||
"Response: and neural activity.\n",
|
||||
"Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive neuroimaging technique that uses near-infrared light to measure changes in cerebral blood oxygenation, which are related to neural activity. Here's how it works:\n",
|
||||
"\n",
|
||||
"1. **Light transmission**: fNIRS uses two wavelengt...\n",
|
||||
"\n",
|
||||
"==========================================================================================\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"new_queries = [\n",
|
||||
" \"What is the relationship between source-detector distance and penetration depth in fNIRS?\",\n",
|
||||
" \"How do chromophores in tissue affect light absorption?\",\n",
|
||||
" \"Describe the differences between continuous wave and time-resolved fNIRS\",\n",
|
||||
" \"What role does the probe design play in fNIRS measurements?\",\n",
|
||||
" \"Explain how fNIRS can be used to study brain hemodynamics\"\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"print(\"Testing Fine-tuned Model with New Queries:\\n\")\n",
|
||||
"print(\"=\" * 90)\n",
|
||||
"\n",
|
||||
"fine_tuned_responses = {}\n",
|
||||
"for query in new_queries:\n",
|
||||
" print(f\"\\nQuery: {query}\")\n",
|
||||
" print(\"-\" * 90)\n",
|
||||
" try:\n",
|
||||
" response = fine_tuned_model.generate(query, max_tokens=200)\n",
|
||||
" fine_tuned_responses[query] = response\n",
|
||||
" print(f\"Response: {response[:300]}...\")\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"Error generating response: {str(e)}\")\n",
|
||||
" fine_tuned_responses[query] = \"Error generating response\"\n",
|
||||
"\n",
|
||||
"print(\"\\n\" + \"=\" * 90)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "a8452857",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"Comparison results saved to: ./build/model_comparison_results.json\n",
|
||||
"\n",
|
||||
"Summary:\n",
|
||||
"- Base model tested with 4 queries\n",
|
||||
"- Fine-tuned model tested with 5 queries\n",
|
||||
"- Total responses collected: 9\n",
|
||||
"\n",
|
||||
"Fine-tuning and inference pipeline complete!\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"comparison_results = {\n",
|
||||
" \"base_model_responses\": base_responses,\n",
|
||||
" \"fine_tuned_model_responses\": fine_tuned_responses,\n",
|
||||
" \"timestamp\": datetime.now().isoformat(),\n",
|
||||
" \"model_config\": {\n",
|
||||
" \"base_model\": BASE_MODEL,\n",
|
||||
" \"learning_rate\": LEARNING_RATE,\n",
|
||||
" \"batch_size\": BATCH_SIZE,\n",
|
||||
" \"epochs\": NUM_EPOCHS,\n",
|
||||
" \"training_samples\": len(training_pairs)\n",
|
||||
" }\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"comparison_file = \"./build/model_comparison_results.json\"\n",
|
||||
"with open(comparison_file, 'w') as f:\n",
|
||||
" json.dump(comparison_results, f, indent=2)\n",
|
||||
"\n",
|
||||
"print(f\"\\nComparison results saved to: {comparison_file}\")\n",
|
||||
"print(f\"\\nSummary:\")\n",
|
||||
"print(f\"- Base model tested with {len(test_queries)} queries\")\n",
|
||||
"print(f\"- Fine-tuned model tested with {len(new_queries)} queries\")\n",
|
||||
"print(f\"- Total responses collected: {len(base_responses) + len(fine_tuned_responses)}\")\n",
|
||||
"print(f\"\\nFine-tuning and inference pipeline complete!\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.13.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
|
@ -10,10 +10,19 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 1,
|
||||
"id": "4c312410",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"c:\\Users\\nalab\\University\\vxn217\\.venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
||||
" from .autonotebook import tqdm as notebook_tqdm\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from gpt4all import GPT4All\n",
|
||||
"from sentence_transformers import SentenceTransformer\n",
|
||||
|
|
@ -29,7 +38,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 2,
|
||||
"id": "90bae527",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -61,7 +70,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 3,
|
||||
"id": "34efbc7c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -84,7 +93,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 4,
|
||||
"id": "ed2cc1ff",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -119,7 +128,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 5,
|
||||
"id": "6fa9fd10",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -145,17 +154,17 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 6,
|
||||
"id": "5a82353e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Frequency-domain (FD) multidistance NIRS technique can estimate absolute values of absorption and scattering of the medium, and subsequently chromophore concentrations.'"
|
||||
"'Frequency-domain (FD) multidistance NIRS technique can estimate absolute values of absorption and scattering of the medium, and subsequently chromophore concentrations. This may involve one or more modulation frequencies.\\n\\nExplanation:\\nThe frequency-domain multidistance NIRS method is a powerful tool for estimating the optical properties of biological tissues in-vivo. By capturing changes in intensity and phase at multiple source-detector separations/distances, this technique can provide absolute values of absorption (μa) and scattering (μs) coefficients. These estimates are crucial for understanding tissue physiology and pathophysiology.\\n\\nThe ability to estimate chromophore concentrations is particularly important as it allows researchers to monitor changes in biomarkers associated with various diseases or physiological processes. This information can be used to develop novel diagnostic tools, track disease progression, and evaluate the effectiveness of therapeutic interventions.\\n\\nIn summary, frequency-domain multidistance NIRS offers a unique combination of sensitivity, specificity, and spatial resolution for non-invasive optical imaging applications. Its ability to estimate absolute'"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in a new issue