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!

📬 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

# 🌱 TinyLLaMA-Sugarcane

Welcome to the *first open-source LLM fine-tuned for sugarcane production*! 🧠🌾

https://huggingface.co/infinitestack

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!

---

## 📬 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

Paste, drop or click to upload images (.png, .jpeg, .jpg, .svg, .gif)