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