Model Card for CropSeek-LLM

CropSeek-LLM is a fine-tuned language model designed to provide insights and recommendations for crop optimization. It is based on the deepseek-ai/DeepSeek-R1-Distill-Qwen-7B model and has been fine-tuned using the DARJYO/sawotiQ29_crop_optimization dataset. The model is optimized for answering questions related to crop planting, soil conditions, pest control, irrigation, and other agricultural practices.

Model Details

Model Description

CropSeek-LLM is a fine-tuned version of the deepseek-ai/DeepSeek-R1-Distill-Qwen-7B model, adapted for crop optimization tasks. It has been trained using LoRA (Low-Rank Adaptation) to efficiently fine-tune the base model on a dataset of crop-related questions and answers. The model is designed to assist farmers, agronomists, and researchers in making informed decisions about crop management.

Developed by: persadian, DARJYO
Model type: Causal Language Model (Fine-tuned with LoRA)
Language(s) (NLP): English
License: DARJYO License v1.0
Finetuned from model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
Hardware used for training: Tesla T4 GPU

Uses

Direct Use

CropSeek-LLM can be used directly to answer questions related to crop optimization, such as:

Optimal planting seasons for specific crops.
Ideal soil conditions for crop growth.
Natural pest control methods.
Best irrigation practices.
Crop rotation strategies.

Downstream Use

CropSeek-LLM can be integrated into agricultural advisory systems, mobile apps, or chatbots to provide real-time recommendations to farmers and agronomists.

Out-of-Scope Use

Medical Advice: This model is not designed to provide medical or health-related advice.
Financial Decisions: The model should not be used for financial or investment decisions.
Non-Agricultural Use: The model is specifically fine-tuned for crop optimization and may not perform well in unrelated domains.

Bias, Risks, and Limitations

Data Bias: The model is trained on a dataset focused on specific crops and regions. It may not generalize well to all crops or geographical areas.
Limited Scope: The model is designed for crop optimization and may not provide accurate answers for unrelated topics.
Ethical Concerns: The model should not replace professional advice from agronomists or agricultural experts.

Recommendations

Users should:

Verify the model's recommendations with local agricultural experts.
Be aware of the model's limitations and use it as a supplementary tool, not a replacement for professional advice.
Report any biases or inaccuracies to the developers for improvement.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the fine-tuned model
model = AutoModelForCausalLM.from_pretrained("persadian/CropSeek-LLM", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("persadian/CropSeek-LLM")

# Example inference
input_text = "What is the best planting season for cabbages in South Coast, Durban?"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

The model was fine-tuned on a curated dataset of agricultural texts, including:

Crop descriptions and classifications.
Plant disease symptoms and treatments.
Farming techniques and best practices.
Regional agricultural guidelines.

Specific dataset used: DARYJO/sawotiQ29_crop_optimization

Training Procedure

Preprocessing

The dataset was cleaned and preprocessed to remove irrelevant information and ensure consistency.
Text data was tokenized using the tokenizer associated with the base model.
Data augmentation techniques, such as synonym replacement and paraphrasing, were applied to improve generalization.

Training Hyperparameters

Training regime: Mixed precision (fp16)
Batch size: 16
Learning rate: 2e-5
Epochs: 3
Optimizer: AdamW
Weight decay: 0.01
Warmup steps: 500

Speeds, Sizes, Times

Training time: Approximately 10 hours on a T4 GPU.
Checkpoint size: 1.5 GB
Throughput: 120 samples/second

Evaluation

Testing Data, Factors & Metrics

Testing Data

The model was evaluated on a held-out test set of agricultural queries, including crop identification, disease diagnosis, and farming recommendations.

[https://huggingface.co/datasets/DARJYO/sawotiQ29_crop_optimization]

Factors

Evaluation was disaggregated by:

Crop type (cereals, fruits, vegetables).
Disease type (fungal, bacterial, viral).
Geographic region (tropical, temperate).

Metrics

Accuracy: 92% on crop identification tasks.
Precision/Recall/F1-score: Precision: 0.89, Recall: 0.91, F1-score: 0.90
Latency: Average response time of 0.5 seconds on a T4 GPU.

Results

The model achieved high accuracy on crop identification and disease diagnosis tasks.
Performance was slightly lower for region-specific recommendations due to limited training data for certain regions.

Summary

CropSeek-LLM performs well on a wide range of agricultural tasks, making it a useful tool for farmers and agricultural professionals. However, performance may vary for rare crops or region-specific practices.

Model Examination

The model was examined using interpretability tools such as attention visualization and feature importance analysis.

Key findings include:

The model relies heavily on symptom descriptions for disease diagnosis.
Crop-specific keywords play a significant role in crop identification tasks.

Environmental Impact

Carbon emissions estimated.

Hardware Type: T4 GPU
Hours used: 10 hours
Cloud Provider: Google Colab
Compute Region: us-central1
Carbon Emitted: Approximately 0.5 kg CO2eq

Technical Specifications

Model Architecture and Objective

Base model architecture: deepseek-ai/deepseek-R1-14B
Objective: Fine-tuned for text generation and classification tasks in the agricultural domain.

Compute Infrastructure

Hardware

Training hardware: Google Colab with T4 GPU.

Software

Frameworks: PyTorch, Hugging Face Transformers.
Libraries: Datasets, Tokenizers, Accelerate.

Citation

BibTeX: @misc{cropseek-llm, author = {persadian~Darshani Persadh, DARJYO}, title = {CropSeek-LLM: A Fine-Tuned Language Model for Agricultural Applications}, year = {2023}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/persadian/CropSeek-LLM}}, }

APA: persadian. Darshani Persadh (2023). CropSeek-LLM: A Fine-Tuned Language Model for Agricultural Applications. Hugging Face. https://huggingface.co/persadian/CropSeek-LLM

Glossary

Mixed precision: Training using both 16-bit and 32-bit floating-point numbers to improve efficiency.

More Information

For more details, visit the CropSeek-LLM space on Hugging Face.

Model Card Authors

persadian ~Darshani Persah

Model Card Contact

[email protected]

persadian
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CropSeek-LLM