| --- |
| license: apache-2.0 |
| datasets: |
| - josedamico/sugarcane |
| language: |
| - en |
| base_model: |
| - TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
| tags: |
| - sugarcane |
| --- |
| # 🌱 TinyLLaMA-Sugarcane |
|
|
| Welcome to the *first open-source LLM fine-tuned for sugarcane production*! 🧠🌾 |
|
|
| This model is a fine-tuned version of [`TinyLLaMA`](https://huggingface.co/czi/TinyLlama-1.1B-Chat-v1.0), trained specifically on sugarcane-focused data. Developed by [SciCrop](https://scicrop.com) as part of its commitment to open innovation in agriculture, this is one of the first domain-specific small language models (SLMs) created for the agribusiness sector. |
|
|
| --- |
|
|
| ## 🚜 Why Sugarcane? |
|
|
| Sugarcane is one of the most important crops in Brazil and globally — but most LLMs know very little about its specific production cycle, challenges, and terminology. |
|
|
| By fine-tuning TinyLLaMA on 2,000+ question/answer pairs from real-world sugarcane use cases, we aim to deliver: |
|
|
| - ✅ Better accuracy |
| - ✅ Clearer answers |
| - ✅ Local deployment capabilities for agricultural experts, cooperatives, and researchers |
|
|
| --- |
|
|
| ## 🔍 Model Details |
|
|
| - **Base model**: `TinyLLaMA-1.1B-Chat` |
| - **Fine-tuned on**: Domain-specific QA pairs related to sugarcane |
| - **Architecture**: Causal LM with LoRA + QLoRA |
| - **Tokenizer**: `LLaMATokenizer` |
| - **Model size**: ~1.1B parameters |
| - **Format**: Available in both HF standard and `GGUF` for local/Ollama use |
|
|
| --- |
|
|
| ## 🧪 Try it locally with Ollama |
|
|
| We believe local models are the future for privacy-sensitive, domain-specific AI. |
|
|
| You can run this model locally using [Ollama](https://ollama.com): |
|
|
| ```bash |
| ollama run infinitestack/tinyllama-sugarcane |
| ``` |
|
|
| 👉 Or explore the model directly: |
| https://ollama.com/infinitestack/tinyllama-sugarcane |
|
|
| --- |
|
|
| ## 🌐 About InfiniteStack |
|
|
| This model is part of **InfiniteStack**, a platform by [SciCrop](https://scicrop.com) that helps companies in the agri-food-energy-environment chain create, train, and deploy their own AI and analytics solutions — securely and at scale. |
|
|
| ### 📦 InfiniteStack offers: |
|
|
| - A containerized platform that runs on-prem or in private cloud |
| - Full support for **SLMs and LLMs** using your **real and private data** |
| - No/Low-code interfaces to *Collect*, *Automate*, *Leverage*, *Catalog*, *Observe*, and *Track* data pipelines and AI assets |
|
|
| 🌐 Learn more: https://infinitestack.ai |
|
|
| --- |
|
|
| ## 🧠 Why Small Language Models (SLMs)? |
|
|
| SLMs are great when: |
|
|
| - You need local inference (offline, on-device, or private) |
| - Your domain is narrow and specific |
| - You want full control over fine-tuning and usage |
| - You care about speed, size, and cost-efficiency |
|
|
| Big isn’t always better. Sometimes, smart and focused beats giant and generic. 💡 |
|
|
| --- |
|
|
| ## 🤝 Community & Open Innovation |
|
|
| This work reflects SciCrop’s ongoing commitment to the open-source ecosystem, and to creating useful, usable AI for real-world agribusiness. |
|
|
| Feel free to fork, contribute, fine-tune further, or use it in your own ag project. |
| We’d love to hear how you're using it! |
|
|
| --- |
|
|
| ## 📂 Files included |
|
|
| This repo includes: |
|
|
| - `config.json` |
| - `tokenizer.model` |
| - `tokenizer.json` |
| - `model.safetensors` |
| - `special_tokens_map.json` |
| - `generation_config.json` |
| - `tokenizer_config.json` |
| - `README.md` |
|
|
| A merged and converted `.gguf` version is also available at **Ollama Hub**. |
|
|
| --- |
|
|
| ## 📬 Questions or Contributions? |
|
|
| Ping us at: |
| 📧 info@scicrop.com |
| 🌐 https://scicrop.com |
| 🌱 https://infinitestack.ai |
|
|
| Made with ☕, 🌾 and ❤️ in Brazil |
| by @josedamico and the InfiniteStack team |
