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