Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more
Container

Run this model inference with full control and performance in your environment.

Learn more

Get help setting up a custom Dedicated Endpoints.

Talk with our engineer to get a quote for reserved GPU instances with discounts.

README

License: apache-2.0

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}} }

Model provider

brimbim

Model tree

Base

mistralai/Mixtral-8x7B-Instruct-v0.1

Adapter

this model

Modalities

Input

Text

Output

Text

Pricing

Dedicated Endpoints

View details

Supported Functionality

Model APIs

Dedicated Endpoints

Container

More information

Explore FriendliAI today