🌱 TinyLLaMA-Sugarcane

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.

You can run this model locally using Ollama:

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 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:
📧 [email protected]
🌐 https://scicrop.com
🌱 https://infinitestack.ai

Made with ☕, 🌾 and ❤️ in Brazil
by @josedamico and the InfiniteStack team

infinitestack
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tinyllama-sugarcane