---
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
library_name: peft
license: apache-2.0
tags:
- agriculture
- crop-disease
- autoscientist-challenge
- lora
- mistral
- peft
- smallholder-farming
language:
- en
---

# Agriculture Crop Disease Advisor v2

**92% win rate** against base model. Built with **AutoScientist Round 2** for the Adaption Labs AutoScientist Challenge (Agriculture Category).

A specialized LoRA adapter for smallholder farmers and agronomists, focused on accurate crop disease diagnosis and practical, low-cost recommendations.

## Model Details

- **Developed by:** brimbim (using Adaption Labs AutoScientist)
- **Model type:** LoRA adapter (PEFT)
- **Base model:** mistralai/Mixtral-8x7B-Instruct-v0.1
- **Language(s):** English
- **License:** Apache 2.0
- **Finetuned from:** mistralai/Mixtral-8x7B-Instruct-v0.1

## Capabilities
- Precise diagnosis from text descriptions of symptoms
- Practical treatment recommendations (strong emphasis on organic & low-cost options)
- Region-aware and climate-resilient advice
- Confidence scores, risk flags, and safety warnings
- Simple, farmer-friendly language

## Training Details

- **Dataset:** ~20,000 high-quality instruction-completion pairs (PlantVillage + synthetic diverse examples)
- **Method:** LoRA (rank 64, alpha 128)
- **Win Rate:** **92%** (adapted vs base model)
- **Quality Score:** 9.8/10 (Grade A)

## Quick Start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "brimbim/agriculture-crop-disease-model-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.bfloat16)

prompt = """You are an expert agronomist helping smallholder farmers.
A farmer in East Africa reports yellow spots with white centers on tomato leaves after heavy rain."""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=400, temperature=0.7, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Uses

### Intended Use:

Helping smallholder farmers and agronomists with crop disease diagnosis and management.

### Out-of-Scope:

Medical advice for humans/animals, large-scale commercial farming systems, or any high-stakes legal/financial decisions.

## Bias, Risks, and Limitations

- Model performance is strongest on common crops (tomato, maize, potato, etc.)
- May have reduced accuracy on rare diseases or crops not well-represented in training data
- Always verify critical recommendations with local agricultural extension services

## Citation

If you use this model, please cite:bibtex

@misc{agriculture-crop-disease-advisor-v2,
  author = {brimbim},
  title = {Agriculture Crop Disease Advisor v2},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/brimbim/agriculture-crop-disease-model-v2}}
}