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| title: README | |
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| <div align="center"> | |
| [](https://pineappleva.github.io/) | |
| # 💻 jAIme Dev Team | |
| **Building useful AI, one model at a time.** | |
|  | |
| [](https://huggingface.co/PineappleVA) | |
| [](https://huggingface.co/) | |
| [](https://github.com/PineappleVA) | |
| [](https://pineappleva.github.io/) | |
| </div> | |
| --- | |
| ## 👋 Hi, we're jAIme Dev Team | |
| We're the artificial intelligence team of [**Pineapple**](https://huggingface.co/PineappleVA), a small project from **Valladolid, Spain** focused on making things a little bit better through technology. | |
| Here we develop **jAIme Models**: artificial intelligence models, experiments and tools designed to be useful, accessible and easy to try. Alongside them, we create the **datasets** and **benchmarks** we use to train, evaluate and improve our work. | |
| Everything starts with a simple idea: AI should be more than a buzzword. It should solve real problems, be understandable and leave room for people to experiment. | |
| | 🤖 jAIme Models | 📚 Datasets | 📊 Benchmarks | 🍍 Pineapple | | |
| | :-: | :-: | :-: | :-: | | |
| | Our AI models and experiments | Data created for training and research | Tools to measure and compare results | The team behind the project | | |
| ### 🎯 What drives us | |
| - 🧠 **Useful AI:** building models with a clear purpose, not AI just for the sake of it. | |
| - 📚 **Open learning:** sharing datasets, benchmarks and experiments whenever possible. | |
| - 🛠️ **Practicality:** creating tools that are simple to understand, test and use. | |
| - ✨ **Experimentation:** trying ideas, learning from what fails and improving what works. | |
| - 🤝 **Community:** making resources that can help students, developers and curious people. | |
| ### 🧭 How we work | |
| From an idea to a model worth using: | |
| 1. **We spot a need:** a problem, an idea or something that could work better with AI. | |
| 2. **We prepare the data:** datasets are the foundation, so we build and organize them carefully. | |
| 3. **We train and test:** we experiment with models, settings and approaches until something starts to make sense. | |
| 4. **We evaluate:** benchmarks help us understand what a model does well and where it still falls short. | |
| 5. **We improve:** every result, error and unexpected answer becomes part of the next version. | |
| --- | |
| ## 🤖 jAIme Models | |
| Our models are the core of the project. Some will be ready to use, others will be experiments in progress, and a few may remain as ideas that taught us something useful. | |
| | Type | What you can expect | | |
| | :-- | :-- | | |
| | 🧠 **Language models** | Models built to understand, generate or work with text in useful ways | | |
| | 🧪 **Experimental models** | Early ideas, prototypes and tests that may grow into bigger projects | | |
| | 🎓 **Educational AI** | Experiments connected to learning, schools and practical classroom needs | | |
| | 🔧 **Tools and demos** | Interactive Spaces and utilities for trying out our work directly in the browser | | |
| We're still building the first pieces of jAIme. Rather than publishing unfinished promises, we'll release models, demos and documentation when they are ready to be explored. | |
| --- | |
| ## 📚 Datasets | |
| Good models need good data. That is why datasets are not just a side project for us: they are part of the work from the beginning. | |
| Our datasets may include material created for: | |
| - 🏷️ Training and fine-tuning jAIme Models. | |
| - 🔎 Testing how models understand, classify or generate content. | |
| - 🎓 Exploring education-related use cases and practical digital tools. | |
| - 🧪 Reproducible experiments that can be improved over time. | |
| When a dataset is released, we aim to explain what it contains, what it is for and any limitations it may have. Data without context is not very useful; data with clear documentation is much more valuable. | |
| --- | |
| ## 📊 Benchmarks and evaluation | |
| A model sounding convincing is not the same as a model working well. We use benchmarks and evaluation tools to compare results, identify weaknesses and make better decisions about future versions. | |
| | We evaluate | Why it matters | | |
| | :-- | :-- | | |
| | 🎯 Accuracy and task performance | To know whether the model actually solves the problem it was built for | | |
| | 🧩 Consistency | To check whether results remain reliable across similar prompts or examples | | |
| | ⚡ Efficiency | To understand how practical a model is to run and use | | |
| | 🛡️ Limitations | To document where the model may fail, hallucinate or need human review | | |
| Benchmarks are not a final scorecard. They are a way of asking better questions: what improved, what got worse and what should we build next? | |
| --- | |
| ## 🤗 What you'll find here | |
| This Hugging Face organization is where we publish the AI side of Pineapple. Over time, you can expect: | |
| - 🧠 **Models** from the jAIme family. | |
| - 🧪 **Spaces** with demos, prototypes and interactive AI tools. | |
| - 📚 **Datasets** used in training, evaluation or research. | |
| - 📊 **Benchmarks** and evaluation resources. | |
| - 📝 Model cards and documentation explaining how each release works. | |
| Some releases will be small, some experimental and some more ambitious. All of them are part of learning how to build AI properly. | |
| --- | |
| ## 🍍 Part of Pineapple | |
| jAIme Dev Team is built by [**Pineapple**](https://huggingface.co/PineappleVA), a team that creates free digital products, websites, tools and games for schools. | |
| The wider Pineapple project focuses on practical technology used in real classrooms. jAIme extends that idea into artificial intelligence: experimenting with models and data while keeping usefulness, accessibility and learning at the center. | |
| [](https://pineappleva.github.io/) | |
| [](https://huggingface.co/PineappleVA) | |
| [](https://github.com/PineappleVA) | |
| --- | |
| ## 🚧 Work in progress | |
| Artificial intelligence is a long-term project for us. We're at an early stage, building the foundation before making big promises. | |
| That means experimenting, making mistakes, rewriting things and taking the time needed to understand what we publish. If a model, dataset or benchmark appears here, it is because it has helped us move one step further. | |
| Follow the organization to keep up with future releases. 👀 | |
| --- | |
| ## 📬 Contact | |
| Questions, ideas, feedback or collaboration proposals? Write to us at **[pineapplevacorp@gmail.com](mailto:pineapplevacorp@gmail.com)**. | |
| You can also follow Pineapple through the [official website](https://pineappleva.github.io/), [GitHub](https://github.com/PineappleVA), [X](https://x.com/pineapplevacorp) and [YouTube](https://www.youtube.com/@pacorp-oficial). | |
| <div align="center"> | |
| [](https://pineappleva.github.io/) | |
| [](https://github.com/PineappleVA) | |
| [](https://huggingface.co/PineappleVA) | |
| [](mailto:pineapplevacorp@gmail.com) | |
| Made with 🍍 and 🤖 in Valladolid · _Building useful AI, one model at a time_ | |
| </div> |