Specialised Tools & Open-Source Models
While commercial AI platforms offer ease and polish, open-source frameworks provide freedom, transparency, and control. These are the engines powering much of today’s innovation in natural language processing (NLP), computer vision (CV), and generative AI. From libraries like spaCy and OpenCV that form the building blocks of applied AI, to large-scale models such as Llama 3 and Mistral 7B ready for fine-tuning, these tools allow researchers, developers, and students to experiment, deploy locally, and contribute to the broader AI ecosystem.
| Framework / Model | Description | Key Features | Link |
|---|---|---|---|
| Hugging Face Transformers | Hub for over 500 000 pre-trained models spanning NLP, vision, and audio tasks. | Zero-shot pipelines; extensive community fine-tuning and hosting tools. | huggingface.co |
| spaCy | Industrial-strength NLP library for tokenisation, entity recognition, and parsing. | Production-ready; easily trainable on local or domain-specific datasets. | spacy.io |
| OpenCV | Leading open-source toolkit for computer vision, object detection, and image processing. | Real-time performance; bindings for Python, C++, and Java. | opencv.org |
| Llama 3 (Meta) | Open large language model for text generation and reasoning, optimised for local fine-tuning. | 8 B–70 B parameters; instruction-tuned and multilingual variants. | huggingface.co/meta-llama/Llama-3 |
| YOLOv8 (Ultralytics) | Fast, real-time object detection framework for research and edge deployment. | Custom training scripts; export to TensorRT, ONNX, and CoreML. | ultralytics.com/yolov8 |
| Mistral 7B | Lightweight open LLM optimised for chat, coding, and efficient inference. | Quantised versions for local use; strong multilingual and reasoning performance. | huggingface.co/mistralai/Mistral-7B |