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Model: mesabo/agri-chat-multilingual
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+- fr
+- es
+- pt
+- sw
+- ja
+- ar
+base_model: Qwen/Qwen2-0.5B-Instruct
+tags:
+- agriculture
+- multilingual
+- chatbot
+- crop-diseases
+- farming
+- west-africa
+pipeline_tag: text-generation
+---
+
+# AgriChat Multilingual - Agricultural Assistant
+
+A multilingual chatbot fine-tuned for agricultural assistance, specifically designed for farmers in West Africa and beyond.
+
+## Model Description
+
+- **Base Model:** Qwen/Qwen2-0.5B-Instruct
+- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
+- **Languages:** English, French, Spanish, Portuguese, Swahili, Japanese, Arabic
+- **Domain:** Agricultural crop diseases, farming practices, pest management
+- **License:** Apache 2.0
+
+## Supported Languages
+
+| Language | Code | Coverage |
+|----------|------|----------|
+| English | en | Full |
+| French | fr | Full |
+| Spanish | es | Full |
+| Portuguese | pt | Full |
+| Swahili | sw | Full |
+| Japanese | ja | Full |
+| Arabic | ar | Full |
+
+## Use Cases
+
+- **Crop Disease Identification:** Ask about symptoms and treatments for plant diseases
+- **Farming Advice:** Get guidance on agricultural practices
+- **Pest Management:** Learn about controlling pests affecting crops
+- **Multilingual Support:** Communicate in 7 different languages
+
+## Quick Start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained("mesabo/agri-chat-multilingual")
+tokenizer = AutoTokenizer.from_pretrained("mesabo/agri-chat-multilingual")
+
+# Chat example
+messages = [
+    {"role": "user", "content": "How do I identify cassava mosaic disease?"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt")
+
+outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## Training Details
+
+- **Training Data:** 38 curated Q&A examples across 7 languages
+- **Epochs:** 3
+- **LoRA Parameters:** 2.16M trainable (0.44% of total)
+- **Training Loss:** 2.54
+- **Hardware:** NVIDIA RTX 3090 (25.3 GB)
+- **Training Time:** ~17 seconds
+
+## Covered Topics
+
+### Crop Diseases
+- Cassava mosaic disease
+- Maize leaf blight
+- Tomato bacterial wilt
+- Cashew anthracnose
+- Rice blast disease
+
+### Farming Practices
+- Organic pest control
+- Soil health management
+- Crop rotation benefits
+- Water conservation
+
+## Limitations
+
+- Fine-tuned on limited agricultural domain data
+- Best suited for common crop diseases in West Africa
+- May not cover specialized or rare conditions
+- Responses should be verified with local agricultural experts
+
+## Intended Use
+
+This model is designed for:
+- Agricultural extension workers
+- Small-scale farmers
+- Agricultural education platforms
+- Farming assistance applications
+
+## Citation
+
+```bibtex
+@misc{agri-chat-multilingual,
+  author = {mesabo},
+  title = {AgriChat Multilingual - Agricultural Assistant},
+  year = {2024},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/mesabo/agri-chat-multilingual}
+}
+```
+
+## Related Models
+
+- [mesabo/agri-plant-disease-resnet50](https://huggingface.co/mesabo/agri-plant-disease-resnet50) - Plant disease image classification (95%+ accuracy)
+
+## Contact
+
+For questions or issues, please open a discussion on the model page.