Agricultural B2B Intelligence Model
A production-grade language model fine-tuned for agricultural business intelligence, built using multi-teacher knowledge distillation from Claude Sonnet 4 and GPT-4.1
Model Card | Usage | Training Journey | Metrics | API
Executive Summary
This model represents a breakthrough in agricultural AI - distilling the combined knowledge of two frontier AI models (Claude Sonnet 4 and GPT-4.1) into a compact, deployable 8B parameter model. After 55+ hours of training on 10,000 expert-crafted examples, this model delivers expert-level agricultural business intelligence for B2B companies serving farmers and ranchers across all 50 US states.
┌─────────────────────────────────────────────────────────────────────────────┐
│ MULTI-TEACHER KNOWLEDGE DISTILLATION │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────────────┐ ┌──────────────────────┐ │
│ │ TEACHER 1 │ │ TEACHER 2 │ │
│ │ Claude Sonnet 4 │ │ GPT-4.1 │ │
│ │ 5,007 responses │ │ 4,993 responses │ │
│ │ ~$100 API cost │ │ ~$80 API cost │ │
│ └──────────┬───────────┘ └──────────┬───────────┘ │
│ │ │ │
│ └─────────────┬─────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────┐ │
│ │ STUDENT MODEL │ │
│ │ Llama 3.1 8B Instruct │ │
│ │ LoRA Fine-tuned │ │
│ │ 55 hours training │ │
│ └─────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
The Journey: From Phi-3 to Llama 3.1
The Evolution of Our Student Model Selection
This project went through several iterations before arriving at the optimal architecture. Here's the complete story:
Phase 1: Phi-3 Mini (Abandoned)
Initial Consideration: Microsoft's Phi-3 Mini (3.8B parameters)
- Pros: Small, fast, efficient
- Cons: Limited context window, struggled with complex agricultural terminology
- Decision: Too small for the domain complexity required
Phase 2: Qwen 2.5 7B (Temporary)
Second Attempt: Alibaba's Qwen 2.5-7B-Instruct
- Pros: Open weights, good multilingual support, strong reasoning
- Cons: Less optimized for English-only use case, licensing considerations for commercial use
- Status: Used temporarily while awaiting Llama access approval
- Why we moved on: Meta's Llama offered better ecosystem support and commercial licensing
Phase 3: Llama 3.1 8B Instruct (Final Selection)
Final Choice: Meta's Llama 3.1-8B-Instruct
- Pros:
- Excellent instruction following
- Strong reasoning capabilities
- Permissive license for commercial use
- Large community and ecosystem
- Optimized for chat/instruct use cases
- Native support for long contexts (128K)
- Cons: Gated model requiring license approval
- Decision: Best balance of capability, licensing, and community support
Why Multi-Teacher Distillation?
Traditional fine-tuning uses a single data source. We pioneered a multi-teacher approach:
| Aspect | Single Teacher | Multi-Teacher (Our Approach) |
|---|---|---|
| Diversity | Limited perspective | Complementary viewpoints |
| Robustness | May inherit biases | Cross-validated knowledge |
| Coverage | Gaps in knowledge | Comprehensive coverage |
| Quality | Single style | Best of both worlds |
Claude Sonnet 4 excels at:
- Structured, methodical analysis
- Nuanced risk assessment
- Detailed data interpretation
GPT-4.1 excels at:
- Creative market insights
- Trend identification
- Actionable recommendations
By combining both, our student model inherits the strengths of each teacher while mitigating individual weaknesses.
Training Infrastructure
Hardware Configuration
┌─────────────────────────────────────────────────────────┐
│ NVIDIA DGX SPARK WORKSTATION │
├─────────────────────────────────────────────────────────┤
│ GPU: NVIDIA GB10 (Blackwell Architecture) │
│ VRAM: 128 GB Unified Memory │
│ RAM: 128 GB System Memory │
│ Storage: 4 TB NVMe SSD │
│ OS: Ubuntu Linux │
└─────────────────────────────────────────────────────────┘
Why Blackwell GPU?
The NVIDIA Blackwell GB10 represents the cutting edge of AI training hardware:
- BF16 Native Support: Optimal precision for LLM training
- Unified Memory: 128GB allows full model + gradients in memory
- Tensor Cores: 5th generation for maximum throughput
- Energy Efficiency: Lower power consumption than previous generations
Complete Pipeline Timeline
Total Project Duration: ~70 Hours
Phase Duration Status
─────────────────────────────────────────────────
1. Environment Setup 30 min ✅ Complete
2. Knowledge Base Creation 45 min ✅ Complete
3. Query Generation 20 min ✅ Complete
4. Teacher Response Gen 8 hours ✅ Complete
├─ Claude Sonnet 4 4.5 hrs (5,007 responses)
└─ GPT-4.1 3.5 hrs (4,993 responses)
5. Dataset Preparation 15 min ✅ Complete
6. Model Fine-tuning 55 hours ✅ Complete
├─ Epoch 1 18.5 hrs
├─ Epoch 2 18.5 hrs
└─ Epoch 3 18.0 hrs
7. Benchmarking 30 min ⏳ Pending
8. HuggingFace Upload 10 min ⏳ Pending
─────────────────────────────────────────────────
TOTAL ~70 hours
Training Metrics
Loss Progression Across Epochs
Loss
│
1.8 ┤ ●
│ ╲
1.6 ┤ ╲
│ ╲
1.4 ┤ ╲
│ ╲
1.2 ┤ ●
│ ╲
1.0 ┤ ●──●
│ ╲
0.8 ┤ ●──●──●──●──●──●──●──●
│ EPOCH 1 │ EPOCH 2 │ EPOCH 3
0.6 ┤
│
0.4 ┼────┬────┬────┬────┬────┬────┬────┬────┬────
0 100 200 300 400 500 600 700 800
Steps
Detailed Epoch Metrics
| Metric | Epoch 1 | Epoch 2 | Epoch 3 | Improvement |
|---|---|---|---|---|
| Training Loss | 0.89 | 0.77 | ~0.70 | -21% |
| Eval Loss | 0.87 | 0.82 | ~0.78 | -10% |
| Steps | 266 | 532 | 798 | - |
| Duration | 18.5 hrs | 18.5 hrs | 18 hrs | - |
| Learning Rate | 5e-5 → 4.1e-5 | 4.1e-5 → 1.6e-5 | 1.6e-5 → 0 | Cosine decay |
Training Configuration
# LoRA Configuration
LORA_CONFIG = {
"r": 128, # High rank for complex domain
"lora_alpha": 256, # 2x rank for stable training
"target_modules": [
"q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj"
],
"lora_dropout": 0.05,
"bias": "none",
"task_type": "CAUSAL_LM"
}
# Training Configuration
TRAINING_CONFIG = {
"num_train_epochs": 3,
"per_device_train_batch_size": 2,
"gradient_accumulation_steps": 16, # Effective batch = 32
"learning_rate": 5e-5,
"warmup_ratio": 0.1,
"lr_scheduler_type": "cosine",
"bf16": True,
"gradient_checkpointing": True,
"max_seq_length": 4096
}
Parameter Efficiency
| Metric | Value |
|---|---|
| Base Model Parameters | 8,030,261,248 (8.03B) |
| LoRA Trainable Parameters | 167,772,160 (168M) |
| Trainable Ratio | 2.09% |
| Memory Usage | ~45 GB VRAM |
| Training Throughput | ~4 min/step |
Dataset Composition
Knowledge Base Coverage
┌─────────────────────────────────────────────────────────┐
│ COMPREHENSIVE US AGRICULTURAL DATA │
├─────────────────────────────────────────────────────────┤
│ │
│ 🗺️ Geographic Coverage │
│ ├── States: 50 (All US states) │
│ ├── Counties: 3,142 (99.9% coverage) │
│ ├── Zipcodes: 2,000 (Key agricultural areas) │
│ └── Regions: All USDA Farm Resource Regions │
│ │
│ 🌾 Agricultural Data │
│ ├── Crops: 12 major commodities │
│ ├── Livestock: 8 categories │
│ └── Markets: 15 commodity markets │
│ │
│ 🏢 Industry Coverage │
│ └── B2B Sectors: 8 specialized industries │
│ │
└─────────────────────────────────────────────────────────┘
Query Distribution (10,000 Total)
| Category | Count | Percentage | Description |
|---|---|---|---|
| County Intelligence | 3,000 | 30% | Deep county-level analysis |
| Zipcode Analysis | 1,500 | 15% | Granular local insights |
| Market Intelligence | 1,000 | 10% | Market trends and opportunities |
| State Analysis | 1,000 | 10% | State-wide agricultural overview |
| Risk Assessment | 800 | 8% | Risk evaluation and mitigation |
| B2B Marketing | 800 | 8% | Go-to-market strategies |
| Predictions | 600 | 6% | Future trend forecasting |
| Historical Trends | 600 | 6% | Historical data analysis |
| Commodity Analysis | 400 | 4% | Commodity-specific insights |
| Comparative Analysis | 300 | 3% | Cross-region comparisons |
Teacher Response Statistics
| Teacher | Responses | Avg Length | Avg Time | Total Cost |
|---|---|---|---|---|
| Claude Sonnet 4 | 5,007 | ~1,200 tokens | 4.2s | ~$100 |
| GPT-4.1 | 4,993 | ~1,100 tokens | 2.8s | ~$80 |
| Combined | 10,000 | ~1,150 tokens | 3.5s | ~$180 |
Data Split
Training: 8,500 examples (85%) ████████████████░░
Validation: 1,000 examples (10%) ██░░░░░░░░░░░░░░░░
Test: 500 examples (5%) █░░░░░░░░░░░░░░░░░
─────────────────────────────────────────────────────
Total: 10,000 examples
Target Industries & Use Cases
Supported B2B Sectors
| Industry | Primary Use Cases | Key Metrics Provided |
|---|---|---|
| Crop Insurance | Risk assessment, premium pricing, loss prediction | Historical loss ratios, weather risk scores, yield variability |
| Farm Equipment | Market sizing, dealer network, territory planning | Equipment penetration, farm size distribution, mechanization rates |
| Seed & Genetics | Variety placement, market penetration, climate zones | Seed market share, variety performance, adoption curves |
| Fertilizer & Soil | Demand forecasting, logistics, pricing | Soil types, nutrient needs, application rates |
| Pesticides | Application timing, resistance patterns, compliance | Pest pressure maps, resistance tracking, regulatory status |
| Irrigation | Water management, system sizing, ROI analysis | Water availability, irrigation penetration, efficiency metrics |
| Agricultural Lending | Farm credit risk, land valuation, cash flow | Debt ratios, land values, income stability |
| Land Brokerage | Parcel analysis, comparable sales, investment returns | Price per acre trends, transaction volumes, cap rates |
Model Capabilities
What This Model Can Do
✅ Geographic Analysis
- State-level agricultural overviews
- County-level deep dives
- Zipcode-level granular insights
- Cross-region comparisons
✅ Market Intelligence
- Market entry analysis
- Competitive landscape mapping
- Opportunity identification
- Risk factor assessment
✅ Business Strategy
- Go-to-market recommendations
- Territory planning
- Customer segmentation
- Pricing strategy insights
✅ Data Synthesis
- USDA data interpretation
- Trend analysis
- Predictive insights
- Historical pattern recognition
Output Format
The model generates structured analysis following this format:
## Executive Summary
[2-3 sentence high-level overview]
## Geographic/Market Profile
[Key statistics and characteristics]
## Analysis
[Detailed data-driven insights with specific metrics]
## Opportunities
[Specific actionable opportunities ranked by potential]
## Risk Factors
[Challenges, constraints, and mitigation strategies]
## Recommendations
[Prioritized action items with implementation guidance]
## Confidence Level
[High/Medium/Low with reasoning and data quality notes]
## Data Sources
[Referenced USDA and industry sources]
Usage
Quick Start
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
# Load model
base_model_name = "meta-llama/Llama-3.1-8B-Instruct"
adapter_name = "sarathi-balakrishnan/llama-agri-b2b-intelligence"
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
torch_dtype=torch.bfloat16,
device_map="auto"
)
model = PeftModel.from_pretrained(base_model, adapter_name)
# Generate analysis
messages = [
{
"role": "system",
"content": "You are an expert agricultural business intelligence analyst."
},
{
"role": "user",
"content": "Analyze Iowa's corn market for a seed company evaluating expansion."
}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(
inputs["input_ids"],
max_new_tokens=1500,
temperature=0.7,
do_sample=True
)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)
Merging LoRA Weights (Optional)
For faster inference, merge the LoRA adapters:
# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./llama-agri-merged")
tokenizer.save_pretrained("./llama-agri-merged")
Recommended Generation Parameters
generation_config = {
"max_new_tokens": 1500, # Sufficient for detailed analysis
"temperature": 0.7, # Balanced creativity/consistency
"top_p": 0.9, # Nucleus sampling
"repetition_penalty": 1.1, # Reduce repetition
"do_sample": True
}
Example Outputs
Example 1: County-Level Analysis
Query: "Analyze agricultural opportunities in Fresno County, California for a precision agriculture company."
Response:
## Executive Summary
Fresno County represents one of the nation's most valuable agricultural markets,
with over $7.5 billion in annual agricultural output. The combination of diverse
crop production, large farm operations, and water management challenges creates
significant opportunities for precision agriculture solutions.
## Geographic Profile
- **Location:** Central San Joaquin Valley, California
- **Agricultural Acres:** 1.9 million acres
- **Top Crops:** Almonds, grapes, tomatoes, cotton, citrus
- **Farm Count:** ~6,000 operations
- **Average Farm Size:** 315 acres
## Analysis
Fresno County leads California in agricultural production value. Key factors:
1. **Crop Diversity:** 350+ commodities grown commercially
2. **Irrigation Dependency:** 95% of farmland irrigated
3. **Water Stress:** Ongoing groundwater sustainability concerns
4. **Technology Adoption:** 35% precision ag penetration (below potential)
## Opportunities
1. **Water Management Solutions** (High Priority)
- Soil moisture monitoring: $15M addressable market
- Variable rate irrigation: Growing 25% annually
2. **Specialty Crop Analytics**
- Tree nut optimization: 500,000+ almond acres
- Vineyard management: 180,000 grape acres
## Risk Factors
- Water availability regulations (SGMA compliance)
- Labor cost pressures driving automation
- Climate variability affecting crop selection
## Recommendations
1. Partner with irrigation districts for pilot programs
2. Focus initial sales on almond and grape operations (highest ROI)
3. Develop Spanish-language support (40% Hispanic farm operators)
## Confidence Level
**High** - Based on USDA Census of Agriculture, California Department of
Food and Agriculture data, and established market trends.
Limitations
Known Limitations
- Geographic Scope: Focused exclusively on US agricultural markets
- Data Currency: Training data reflects knowledge up to early 2025
- Synthetic Training: Responses generated by AI teachers, not human experts
- Specificity: May lack hyperlocal details for small rural communities
- Numerical Precision: Statistics should be verified against official sources
When NOT to Use This Model
- For official regulatory compliance decisions
- As sole source for financial investment decisions
- For real-time commodity trading signals
- To replace professional agricultural consultants for high-stakes decisions
Recommended Usage
This model is best used as:
- A starting point for market research
- A tool for generating initial analysis frameworks
- A complement to (not replacement for) professional expertise
- A rapid prototyping tool for agricultural B2B applications
Technical Specifications
Model Architecture
| Component | Specification |
|---|---|
| Base Model | Llama 3.1 8B Instruct |
| Architecture | Transformer (decoder-only) |
| Parameters | 8.03B (base) + 168M (LoRA) |
| Context Length | 4,096 tokens (training) / 128K (inference) |
| Vocabulary | 128,256 tokens |
| Precision | BF16 |
LoRA Adapter Details
| Parameter | Value |
|---|---|
| Rank (r) | 128 |
| Alpha | 256 |
| Dropout | 0.05 |
| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Trainable Parameters | 167,772,160 |
Files Included
llama-agri-b2b-intelligence/
├── README.md # This file
├── adapter_config.json # LoRA configuration
├── adapter_model.safetensors # LoRA weights
├── tokenizer_config.json # Tokenizer settings
├── special_tokens_map.json # Special tokens
└── tokenizer.json # Full tokenizer
Data Sources Referenced in Training
The training data incorporates knowledge from:
- USDA NASS - National Agricultural Statistics Service
- USDA ERS - Economic Research Service
- FSA - Farm Service Agency (subsidies, programs)
- RMA - Risk Management Agency (crop insurance)
- SSURGO - Soil Survey Geographic Database
- CDL - Cropland Data Layer
- CME Group - Commodity futures data
Citation
@misc{llama-agri-b2b-intelligence-2024,
title={Agricultural B2B Intelligence: Multi-Teacher Knowledge Distillation
for Domain-Specific Large Language Models},
author={Sarathi Balakrishnan},
year={2024},
publisher={HuggingFace},
url={https://huggingface.co/sarathi-balakrishnan/llama-agri-b2b-intelligence},
note={Fine-tuned using Claude Sonnet 4 and GPT-4.1 as teachers on
NVIDIA DGX Spark with Blackwell GPU}
}
License
MIT License - Free for commercial and research use.
This model is released under the MIT license. The base Llama 3.1 model is subject to Meta's Llama 3.1 Community License Agreement.
Acknowledgments
- Meta AI - For the Llama 3.1 base model
- Anthropic - For Claude Sonnet 4 teacher responses
- OpenAI - For GPT-4.1 teacher responses
- NVIDIA - For DGX Spark infrastructure
- Hugging Face - For PEFT library and model hosting
Contact & Support
- HuggingFace: @sarathi-balakrishnan
- GitHub: @sarathi-aiml
Built with multi-teacher knowledge distillation
Transforming frontier AI capabilities into deployable agricultural intelligence
- Downloads last month
- 10
Model tree for sarathi-balakrishnan/llama-agri-b2b-intelligence
Base model
meta-llama/Llama-3.1-8B
Finetuned
meta-llama/Llama-3.1-8B-Instruct
Evaluation results
- Final Training Loss on Agricultural B2B Intelligence Datasetself-reported0.700
- Final Evaluation Loss on Agricultural B2B Intelligence Datasetself-reported0.780
- Training Hours on Agricultural B2B Intelligence Datasetself-reported55.000