You can deploy this model and start earning money today!

kunaliitkgp09/
agriculture-specialist-gpt2

Name: agriculture-specialist-gpt2
Availability: InStock

Views

No views yet

Agriculture Specialist GPT-2 Model

This is a fine-tuned GPT-2 model specialized in agriculture and farming knowledge. The model has been trained on a comprehensive dataset of agriculture-related Q&A pairs to provide expert-level responses to farming questions.

Model Details

Developed by: Kunal Dhanda
Model type: Causal Language Model (GPT-2)
Language: English
License: MIT
Finetuned from model: gpt2
Repository: FineTuneAgriculturistSpecialist
Paper: Fine-tuning GPT-2 for Agriculture Q&A using Instruction Tuning
Demo: Try the model online

Model Description

This model is a fine-tuned version of GPT-2 specifically designed for agriculture and farming applications. It has been trained on a diverse dataset of 53 JSONL files containing agriculture knowledge covering topics such as:

Crop management and rotation
Soil health and testing
Pest control and management
Irrigation methods (including drip irrigation)
Organic farming practices
Plant nutrition and fertilizers
Sustainable agriculture
Water management
Seed germination and planting
Agricultural biotechnology

Intended Uses & Limitations

Intended Uses

Agriculture education and training
Farming consultation and advice
Agricultural research assistance
Crop management guidance
Sustainable farming practices

Limitations

The model is specifically tuned for agriculture topics
Responses are based on the training data quality and coverage
May not be suitable for non-agriculture related questions
Should not be used as the sole source for critical farming decisions
Always consult with agricultural experts for important farming decisions

Training and Evaluation Data

Training Data

Dataset: Custom agriculture Q&A dataset
Format: JSONL files with instruction/input/output format
Size: 53 files with agriculture-specific content
Topics: Comprehensive coverage of farming and agriculture topics

Training Infrastructure

Hardware: CPU-based training (macOS with Apple Silicon)
Framework: Hugging Face Transformers
Training Method: Instruction tuning with agriculture Q&A data

Training Results

The model shows significant improvement over the base GPT-2 model in agriculture-related questions, providing more relevant and accurate responses to farming queries. The fine-tuning process focused on:

Better understanding of agriculture terminology
Improved response quality for farming questions
Enhanced knowledge of sustainable practices
More accurate technical information

Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model
tokenizer = AutoTokenizer.from_pretrained("kunaliitkgp09/agriculture-specialist-gpt2")
model = AutoModelForCausalLM.from_pretrained("kunaliitkgp09/agriculture-specialist-gpt2")

# Set pad token
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

# Example usage
question = "What are the best practices for organic farming?"
prompt = f"### Instruction:
{question}

### Response:
"

inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=200,
        temperature=0.8,
        do_sample=True,
        top_p=0.9,
        top_k=50,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id,
        repetition_penalty=1.1,
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Environmental Impact

Hardware Type: CPU-based training (Apple Silicon M1/M2)
Hours used: ~2-3 hours for complete fine-tuning
Cloud Provider: Local training
Compute Region: Local development
Carbon Emitted: Minimal (local CPU training)

Citation

If you use this model in your research or applications, please cite:

bibtex

@misc{agriculture-specialist-gpt2,
  title={Agriculture Specialist GPT-2 Model},
  author={Kunal Dhanda},
  year={2024},
  url={https://huggingface.co/kunaliitkgp09/agriculture-specialist-gpt2},
  note={Fine-tuned GPT-2 model for agriculture Q&A}
}

Acknowledgments

Hugging Face for the transformers library
OpenAI for the base GPT-2 model
The agriculture community for providing valuable knowledge
Open source contributors who made this project possible

Model Card Contact

For questions about this model, please contact:

Creator: Kunal Dhanda
Repository: FineTuneAgriculturistSpecialist
Model Page: Hugging Face