146 lines
16 KiB
Text
146 lines
16 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0910db83",
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"metadata": {},
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"source": [
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"# Model Testing with GPT4ALL running locally"
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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": "47cacfc9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Imports\n",
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"import json\n",
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"import requests"
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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": "484cfebc",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Variables for model response\n",
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"API_URL = \"http://localhost:4891/v1/chat/completions\"\n",
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"HEADERS = {\"Content-Type\": \"application/json\"}\n",
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"MODEL = \"DeepSeek-R1-Distill-Qwen-7B\"\n",
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"MAX_TOKENS = 2000\n",
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"TEMPERATURE = 0.28"
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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": 8,
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"id": "90b9b1f1",
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"metadata": {},
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"outputs": [],
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"source": [
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"content = \"Teach me computer vision\"\n",
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"data = {\"model\": MODEL,\"messages\":[{\"role\":\"user\",\"content\": content}],\"max_tokens\": MAX_TOKENS,\"temperature\": TEMPERATURE}\n",
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"response = requests.post(API_URL, json = data, headers=HEADERS)"
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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": 9,
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"id": "88a77498",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'{\"choices\":[{\"finish_reason\":\"stop\",\"index\":0,\"logprobs\":null,\"message\":{\"content\":\"<think>\\\\n\\\\n</think>\\\\n\\\\nComputer vision is a field of artificial intelligence that focuses on enabling computers to interpret and understand visual information from the world. It involves training algorithms, typically using deep learning techniques, to perform tasks such as object recognition, image segmentation, feature extraction, and more.\\\\n\\\\nHere’s an introduction to get you started:\\\\n\\\\n---\\\\n\\\\n### **1. What is Computer Vision?**\\\\nComputer vision mimics human visual perception by analyzing images or video data to extract meaningful information. It relies heavily on machine learning and deep learning techniques like convolutional neural networks (CNNs) to perform tasks such as:\\\\n- Object detection\\\\n- Image classification\\\\n- Face recognition\\\\n- Medical image analysis\\\\n- Autonomous vehicle navigation\\\\n\\\\n---\\\\n\\\\n### **2. Key Concepts in Computer Vision**\\\\n#### **Image Representation**\\\\n- **Pixels**: The basic unit of an image, represented by numerical values indicating color and brightness.\\\\n- **Channels**: Color images have multiple channels (e.g., RGB has red, green, blue channels).\\\\n\\\\n#### **Common Tasks**\\\\n1. **Object Detection**:\\\\n - Identify the presence and location of objects in an image.\\\\n - Example: Bounding box regression.\\\\n\\\\n2. **Classification**:\\\\n - Categorize images into predefined classes (e.g., cat vs. dog).\\\\n\\\\n3. **Segmentation**:\\\\n - Partition an image into segments, each representing a different object or region.\\\\n\\\\n4. **Feature Extraction**:\\\\n - Identify and extract relevant patterns from images for further analysis.\\\\n\\\\n---\\\\n\\\\n### **3. Tools and Libraries**\\\\nTo get started with computer vision, you’ll need tools like OpenCV (Open Source Computer Vision) or TensorFlow/Keras for building models.\\\\n\\\\n#### **OpenCV**\\\\n- A popular open-source library for image processing.\\\\n- Features:\\\\n - Image filtering\\\\n - Edge detection\\\\n - Object tracking\\\\n - Face recognition\\\\n\\\\n#### **TensorFlow/Keras**\\\\n- Frameworks built on top of TensorFlow, ideal for deep learning tasks.\\\\n- Easy to use and widely adopted.\\\\n\\\\n---\\\\n\\\\n### **4. Getting Started with Computer Vision**\\\\n\\\\n#### **Step 1: Learn the Basics**\\\\nStart by understanding fundamental concepts like pixels, image processing techniques, and basic computer vision algorithms (e.g., SIFT, HOG).\\\\n\\\\n#### **Step 2: Explore Datasets**\\\\nWork with common datasets:\\\\n- CIFAR-10/100\\\\n- MNIST (handwritten digits)\\\\n- COCO (common objects in context)\\\\n\\\\n#### **Step 3: Build Simple Models**\\\\nUse pre-trained models like ResNet or VGG to classify images. For example, you can train a model to recognize cats vs. dogs.\\\\n\\\\n#### **Step 4: Experiment with Deep Learning**\\\\nTune hyperparameters (learning rate, batch size) and explore techniques like data augmentation to improve model performance.\\\\n\\\\n---\\\\n\\\\n### **5. Resources for Learning**\\\\n- **Books**:\\\\n - *Deep Learning for Computer Vision* by Adrian Rosebrock\\\\n - *Computer Vision: Algorithms and Applications* by Richard Szeliski\\\\n\\\\n- **Tutorials/Documentation**:\\\\n - OpenCV官网文档 [https://docs.opencv.org](https://docs.opencv.org)\\\\n - TensorFlow/Keras官网文档 [https://www.tensorflow.org](https://www.tensorflow.org)\\\\n\\\\n- **Online Courses**:\\\\n - Coursera: \\\\\"Introduction to Computer Vision\\\\\" by Georgia Tech\\\\n - Udacity: \\\\\"Deep Learning for Computer Vision\\\\\"\\\\n - Fast.ai: Free, practical courses on computer vision.\\\\n\\\\n---\\\\n\\\\n### **6. Practice Projects**\\\\n1. **Object Detection**: Use YOLO or Mask R-CNN to detect objects in images.\\\\n2. **Image Classification**: Build a model that classifies images into predefined categories (e.g., flowers vs. vegetables).\\\\n3. **Face Recognition**: Implement face recognition using deep learning frameworks.\\\\n\\\\n---\\\\n\\\\n### **7. Keep Learning**\\\\n- Follow research papers on arXiv ([https://arxiv.org](https://arxiv.org)).\\\\n- Join communities like Reddit’s r/computervision or Stack Overflow.\\\\n- Experiment with cutting-edge models and techniques in computer vision.\\\\n\\\\n---\\\\n\\\\nWith practice and persistence, you’ll become proficient in computer vision. Start small, experiment, and most importantly, have fun!\",\"role\":\"assistant\"},\"references\":null}],\"created\":1768678056,\"id\":\"placeholder\",\"model\":\"DeepSeek-R1-Distill-Qwen-7B\",\"object\":\"chat.completion\",\"usage\":{\"completion_tokens\":861,\"prompt_tokens\":8,\"total_tokens\":869}}'"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"response.text"
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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": 10,
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"id": "c416905c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'choices': [{'finish_reason': 'stop',\n",
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" 'index': 0,\n",
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" 'logprobs': None,\n",
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" 'message': {'content': '<think>\\n\\n</think>\\n\\nComputer vision is a field of artificial intelligence that focuses on enabling computers to interpret and understand visual information from the world. It involves training algorithms, typically using deep learning techniques, to perform tasks such as object recognition, image segmentation, feature extraction, and more.\\n\\nHere’s an introduction to get you started:\\n\\n---\\n\\n### **1. What is Computer Vision?**\\nComputer vision mimics human visual perception by analyzing images or video data to extract meaningful information. It relies heavily on machine learning and deep learning techniques like convolutional neural networks (CNNs) to perform tasks such as:\\n- Object detection\\n- Image classification\\n- Face recognition\\n- Medical image analysis\\n- Autonomous vehicle navigation\\n\\n---\\n\\n### **2. Key Concepts in Computer Vision**\\n#### **Image Representation**\\n- **Pixels**: The basic unit of an image, represented by numerical values indicating color and brightness.\\n- **Channels**: Color images have multiple channels (e.g., RGB has red, green, blue channels).\\n\\n#### **Common Tasks**\\n1. **Object Detection**:\\n - Identify the presence and location of objects in an image.\\n - Example: Bounding box regression.\\n\\n2. **Classification**:\\n - Categorize images into predefined classes (e.g., cat vs. dog).\\n\\n3. **Segmentation**:\\n - Partition an image into segments, each representing a different object or region.\\n\\n4. **Feature Extraction**:\\n - Identify and extract relevant patterns from images for further analysis.\\n\\n---\\n\\n### **3. Tools and Libraries**\\nTo get started with computer vision, you’ll need tools like OpenCV (Open Source Computer Vision) or TensorFlow/Keras for building models.\\n\\n#### **OpenCV**\\n- A popular open-source library for image processing.\\n- Features:\\n - Image filtering\\n - Edge detection\\n - Object tracking\\n - Face recognition\\n\\n#### **TensorFlow/Keras**\\n- Frameworks built on top of TensorFlow, ideal for deep learning tasks.\\n- Easy to use and widely adopted.\\n\\n---\\n\\n### **4. Getting Started with Computer Vision**\\n\\n#### **Step 1: Learn the Basics**\\nStart by understanding fundamental concepts like pixels, image processing techniques, and basic computer vision algorithms (e.g., SIFT, HOG).\\n\\n#### **Step 2: Explore Datasets**\\nWork with common datasets:\\n- CIFAR-10/100\\n- MNIST (handwritten digits)\\n- COCO (common objects in context)\\n\\n#### **Step 3: Build Simple Models**\\nUse pre-trained models like ResNet or VGG to classify images. For example, you can train a model to recognize cats vs. dogs.\\n\\n#### **Step 4: Experiment with Deep Learning**\\nTune hyperparameters (learning rate, batch size) and explore techniques like data augmentation to improve model performance.\\n\\n---\\n\\n### **5. Resources for Learning**\\n- **Books**:\\n - *Deep Learning for Computer Vision* by Adrian Rosebrock\\n - *Computer Vision: Algorithms and Applications* by Richard Szeliski\\n\\n- **Tutorials/Documentation**:\\n - OpenCV官网文档 [https://docs.opencv.org](https://docs.opencv.org)\\n - TensorFlow/Keras官网文档 [https://www.tensorflow.org](https://www.tensorflow.org)\\n\\n- **Online Courses**:\\n - Coursera: \"Introduction to Computer Vision\" by Georgia Tech\\n - Udacity: \"Deep Learning for Computer Vision\"\\n - Fast.ai: Free, practical courses on computer vision.\\n\\n---\\n\\n### **6. Practice Projects**\\n1. **Object Detection**: Use YOLO or Mask R-CNN to detect objects in images.\\n2. **Image Classification**: Build a model that classifies images into predefined categories (e.g., flowers vs. vegetables).\\n3. **Face Recognition**: Implement face recognition using deep learning frameworks.\\n\\n---\\n\\n### **7. Keep Learning**\\n- Follow research papers on arXiv ([https://arxiv.org](https://arxiv.org)).\\n- Join communities like Reddit’s r/computervision or Stack Overflow.\\n- Experiment with cutting-edge models and techniques in computer vision.\\n\\n---\\n\\nWith practice and persistence, you’ll become proficient in computer vision. Start small, experiment, and most importantly, have fun!',\n",
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" 'role': 'assistant'},\n",
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" 'references': None}],\n",
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" 'created': 1768678056,\n",
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" 'id': 'placeholder',\n",
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" 'model': 'DeepSeek-R1-Distill-Qwen-7B',\n",
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" 'object': 'chat.completion',\n",
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" 'usage': {'completion_tokens': 861, 'prompt_tokens': 8, 'total_tokens': 869}}"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"response_data = json.loads(response.text)\n",
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"response_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": 11,
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"id": "2553d924",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'<think>\\n\\n</think>\\n\\nComputer vision is a field of artificial intelligence that focuses on enabling computers to interpret and understand visual information from the world. It involves training algorithms, typically using deep learning techniques, to perform tasks such as object recognition, image segmentation, feature extraction, and more.\\n\\nHere’s an introduction to get you started:\\n\\n---\\n\\n### **1. What is Computer Vision?**\\nComputer vision mimics human visual perception by analyzing images or video data to extract meaningful information. It relies heavily on machine learning and deep learning techniques like convolutional neural networks (CNNs) to perform tasks such as:\\n- Object detection\\n- Image classification\\n- Face recognition\\n- Medical image analysis\\n- Autonomous vehicle navigation\\n\\n---\\n\\n### **2. Key Concepts in Computer Vision**\\n#### **Image Representation**\\n- **Pixels**: The basic unit of an image, represented by numerical values indicating color and brightness.\\n- **Channels**: Color images have multiple channels (e.g., RGB has red, green, blue channels).\\n\\n#### **Common Tasks**\\n1. **Object Detection**:\\n - Identify the presence and location of objects in an image.\\n - Example: Bounding box regression.\\n\\n2. **Classification**:\\n - Categorize images into predefined classes (e.g., cat vs. dog).\\n\\n3. **Segmentation**:\\n - Partition an image into segments, each representing a different object or region.\\n\\n4. **Feature Extraction**:\\n - Identify and extract relevant patterns from images for further analysis.\\n\\n---\\n\\n### **3. Tools and Libraries**\\nTo get started with computer vision, you’ll need tools like OpenCV (Open Source Computer Vision) or TensorFlow/Keras for building models.\\n\\n#### **OpenCV**\\n- A popular open-source library for image processing.\\n- Features:\\n - Image filtering\\n - Edge detection\\n - Object tracking\\n - Face recognition\\n\\n#### **TensorFlow/Keras**\\n- Frameworks built on top of TensorFlow, ideal for deep learning tasks.\\n- Easy to use and widely adopted.\\n\\n---\\n\\n### **4. Getting Started with Computer Vision**\\n\\n#### **Step 1: Learn the Basics**\\nStart by understanding fundamental concepts like pixels, image processing techniques, and basic computer vision algorithms (e.g., SIFT, HOG).\\n\\n#### **Step 2: Explore Datasets**\\nWork with common datasets:\\n- CIFAR-10/100\\n- MNIST (handwritten digits)\\n- COCO (common objects in context)\\n\\n#### **Step 3: Build Simple Models**\\nUse pre-trained models like ResNet or VGG to classify images. For example, you can train a model to recognize cats vs. dogs.\\n\\n#### **Step 4: Experiment with Deep Learning**\\nTune hyperparameters (learning rate, batch size) and explore techniques like data augmentation to improve model performance.\\n\\n---\\n\\n### **5. Resources for Learning**\\n- **Books**:\\n - *Deep Learning for Computer Vision* by Adrian Rosebrock\\n - *Computer Vision: Algorithms and Applications* by Richard Szeliski\\n\\n- **Tutorials/Documentation**:\\n - OpenCV官网文档 [https://docs.opencv.org](https://docs.opencv.org)\\n - TensorFlow/Keras官网文档 [https://www.tensorflow.org](https://www.tensorflow.org)\\n\\n- **Online Courses**:\\n - Coursera: \"Introduction to Computer Vision\" by Georgia Tech\\n - Udacity: \"Deep Learning for Computer Vision\"\\n - Fast.ai: Free, practical courses on computer vision.\\n\\n---\\n\\n### **6. Practice Projects**\\n1. **Object Detection**: Use YOLO or Mask R-CNN to detect objects in images.\\n2. **Image Classification**: Build a model that classifies images into predefined categories (e.g., flowers vs. vegetables).\\n3. **Face Recognition**: Implement face recognition using deep learning frameworks.\\n\\n---\\n\\n### **7. Keep Learning**\\n- Follow research papers on arXiv ([https://arxiv.org](https://arxiv.org)).\\n- Join communities like Reddit’s r/computervision or Stack Overflow.\\n- Experiment with cutting-edge models and techniques in computer vision.\\n\\n---\\n\\nWith practice and persistence, you’ll become proficient in computer vision. Start small, experiment, and most importantly, have fun!'"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"response_data['choices'][0]['message']['content']"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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