Welcome to the first open-source LLM fine-tuned for sugarcane production! 🧠🌾
This model is a fine-tuned version of TinyLLaMA, trained specifically on sugarcane-focused data. Developed by SciCrop 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.
This model is part of InfiniteStack, a platform by SciCrop 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
