sarathi-balakrishnan/phi3-agricultural-analyst

Phi-3 Agricultural Analyst

A domain-specific language model fine-tuned for agricultural analysis tasks. This model provides structured insights on farm operations, investment opportunities, risk assessment, and regional agricultural profiling.

Project Scope

This release focuses on 8 major US agricultural counties as a proof-of-concept:

• Fresno, CA (Tree nuts, Grapes)
• Kern, CA (Almonds, Pistachios)
• Lancaster, PA (Dairy, Corn)
• Sioux, IA (Corn, Soybeans)
• Yakima, WA (Apples, Hops)
• Imperial, CA (Alfalfa, Lettuce)
• Monterey, CA (Strawberries, Lettuce)
• Deaf Smith, TX (Cattle, Wheat)

Upcoming releases will include:

• Full US county coverage (3,000+ counties)
• Specialized models for crop yield prediction
• Harvest timing optimization
• Soil health analysis
• Weather impact assessment

Performance Benchmarks

Evaluated against base Phi-3 Mini on agricultural analysis tasks:

Content Coverage

Base Model    |████████████████████                    | 50.0%
Fine-Tuned    |█████████████████████████               | 63.3%  (+26.7%)

Structure Quality

Base Model    |██████████████████████                  | 56.7%
Fine-Tuned    |█████████████████████████████████       | 83.3%  (+47.1%)

Inference Speed

Base Model    |████████████████████████████████████████| 101.9s
Fine-Tuned    |████████████████████████                | 62.4s  (38.8% faster)

Summary Table

Metric	Base Model	Fine-Tuned	Improvement
Content Coverage	50.0%	63.3%	+26.7%
Structure Quality	56.7%	83.3%	+47.1%
Avg Inference Time	101.9s	62.4s	38.8% faster

Training Loss Curve

Epoch 1: ████████████████████████████████████████ Loss: 1.605 → 0.115
Epoch 2: ████████████                             Loss: 0.115 → 0.020
Epoch 3: ████                                     Loss: 0.020 → 0.019

The fine-tuned model shows significant improvement in generating domain-relevant content and producing well-structured analytical outputs.

Training Configuration

Parameter	Value
Base Model	microsoft/Phi-3-mini-4k-instruct
Method	LoRA (Low-Rank Adaptation)
LoRA Rank	64
LoRA Alpha	128
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Training Examples	900
Validation Examples	100
Epochs	3
Batch Size	16
Learning Rate	2e-4
Precision	BF16
Training Time	~51 minutes
Final Eval Loss	0.0186
Token Accuracy	99.3%

Hardware: NVIDIA DGX Spark (Blackwell GPU, 128GB RAM)

Use Cases

• Farm investment analysis
• Regional agricultural profiling
• Risk factor identification
• Technology adoption recommendations
• Irrigation and water sustainability assessment
• Crop expansion planning

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# Load model
model_name = "microsoft/Phi-3-mini-4k-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

base_model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
model = PeftModel.from_pretrained(base_model, "sarathi-balakrishnan/phi3-agricultural-analyst")

# Create prompt
prompt = """You are an expert agricultural analyst. Analyze the following query using the provided data context.

### Query
What are the key investment opportunities in Fresno County agriculture?

### Data Context
County: Fresno, CA
Operators: 5,847
Average Farm Size: 423 acres
Irrigation Coverage: 89%
Revenue per Acre: $2,340

### Analysis"""

# Generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    inputs["input_ids"],
    max_new_tokens=500,
    temperature=0.7,
    do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Output Format

The model generates structured analysis with:

• Summary of the agricultural region
• Key insights based on data patterns
• Investment/growth opportunities
• Risk factors and constraints
• Confidence level

Limitations

• Currently covers 8 US counties (expansion planned)
• Training data is synthetically generated
• Should not replace professional agricultural consulting
• Performance may vary for regions outside training distribution

Technical Notes

• Uses eager attention for Phi-3 compatibility
• Optimized for BF16 inference on modern GPUs
• LoRA adapters can be merged for deployment: model.merge_and_unload()

License

MIT License - Free for commercial and research use.

Contact

For questions about this model or collaboration on agricultural AI:

• HuggingFace: @sarathi-balakrishnan