DarleisonBarrosFilho/sfcc-commerce-expert-v1

CommerceExpert v1

Fine-tuned LLMs for Salesforce Commerce Cloud error classification

Three open-source models (Llama-3.2-1B, LFM-2.5-Thinking, Qwen-2.5-7B) trained on curated SFCC error data for real-time error categorization and event classification.

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📋 Overview

CommerceExpert classifies SFCC runtime errors into standard categories. Choose the model that fits your deployment:

• Llama-3.2-1B — Lightweight, edge deployment (Cloudflare Workers, on-device)
• LFM-2.5-Thinking — Reasoning-focused, explainable predictions (audit trails)
• Qwen-2.5-7B — Highest accuracy, GPU-required (cloud, batch processing)

All three models are trained on the same dataset and ready for production integration.

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🎯 Use Cases

✅ Real-time SFCC error classification
✅ Automated error triage and categorization
✅ Integration with alerting systems
✅ Event-driven ecommerce analytics
✅ Compliance and audit logging (with LFM reasoning output)

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📊 Model Details

Property	Value
Training Data	1,000 SFCC errors (200 real + 800 synthetic)
Eval Data	300 held-out examples
Error Classes	9 categories (Network, Auth, Quota, Validation, NotFound, Payment, InternalError, BusinessLogic, Integration)
Fine-Tune Method	LoRA (r=16, alpha=32)
Training Epochs	1 (smoke test)
Format	Alpaca (Llama, Qwen), ChatML (LFM)
License	Apache 2.0

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🚀 Quick Start

Installation

pip install transformers peft unsloth torch

Load & Infer (Llama)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="darleison/CommerceExpert-Llama-v1",
    max_seq_length=2048,
    load_in_4bit=True,
)
model = FastLanguageModel.for_inference(model)

# Classify an error
error_json = '{"code": "ECONNREFUSED", "message": "Connection refused", "context": "api-gateway"}'
prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
Classify the following Salesforce Commerce Cloud runtime error into a standard category.

### Input:
{error_json}

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=16)
result = tokenizer.batch_decode(outputs)[0]
print(result)  # → "Network"

Load & Infer (Qwen)

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="darleison/CommerceExpert-Qwen-v1",
    max_seq_length=4096,
    load_in_4bit=True,
)
# Same inference as above

Load & Infer (LFM - with reasoning)

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="darleison/CommerceExpert-LFM-v1",
    max_seq_length=4096,
    load_in_4bit=True,
)
# Same inference, longer output includes reasoning chain
outputs = model.generate(**inputs, max_new_tokens=256)

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Error Categories

The model predicts one of 9 error classes:

1. Network — Connection, DNS, timeout errors
2. Authentication — Auth failures, invalid tokens, permission denied
3. Quota — Rate limits, API quotas, concurrent limits
4. Validation — JSON, schema, required field errors
5. NotFound — 404, missing resources
6. Payment — Payment processing failures
7. InternalError — Server errors, crashes, exceptions
8. BusinessLogic — Out of stock, invalid coupon, shipping unavailable
9. Integration — Webhook failures, external service errors

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📈 Performance

Accuracy (on 300-example golden test set)

Model	Baseline	Fine-Tuned	Improvement
Llama-3.2-1B	[INSERT]%	[INSERT]%	[INSERT]+%
LFM-2.5-Thinking	[INSERT]%	[INSERT]%	[INSERT]+%
Qwen-2.5-7B	[INSERT]%	[INSERT]%	[INSERT]+%

Results from evaluation on held-out test set. Baseline = zero-shot pre-trained model.

Inference Speed

Model	Tokens/sec (GPU)	Tokens/sec (CPU)	Model Size
Llama-3.2-1B	~500	~50	~650 MB
LFM-2.5-Thinking	~300	~30	~650 MB
Qwen-2.5-7B	~100	~10	~3.5 GB

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⚙️ Training Configuration

LoRA:
  rank: 16
  alpha: 32
  dropout: 0
  target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]

Training:
  epochs: 1
  batch_size: 2 (per device)
  gradient_accumulation_steps: 4
  learning_rate: 2e-4
  optimizer: adamw_8bit
  scheduler: linear
  warmup_steps: 5
  weight_decay: 0.01

Data:
  seed: 42 (reproducible)
  train: 1000 examples (800 synthetic, 200 real)
  test: 300 examples (held-out)

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🔬 Data Generation

Training data expanded from seed dataset using:

1. Error Taxonomy — 9 categories with subcategories
2. Synthetic Generation — Realistic error patterns from templates
3. Cartridge Integration — Error codes from 18 real SFCC integrations (payment, shipping, fraud, etc.)
4. B2C Patterns — Real log examples and error formats

Final dataset: balanced class distribution, diverse error contexts, realistic for production SFCC systems.

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📦 Integration with PulsarJS

CommerceExpert integrates as the ML classification layer in PulsarJS, an event-centric knowledge graph platform for ecommerce attribution.

from unsloth import FastLanguageModel

class CommerceExpertClassifier:
    def __init__(self, model_name="llama"):
        models = {
            "llama": "darleison/CommerceExpert-Llama-v1",
            "qwen": "darleison/CommerceExpert-Qwen-v1",
            "lfm": "darleison/CommerceExpert-LFM-v1",
        }
        self.model, self.tokenizer = FastLanguageModel.from_pretrained(
            model_name=models[model_name],
            max_seq_length=4096,
            load_in_4bit=True,
        )
        self.model = FastLanguageModel.for_inference(self.model)

    def classify(self, error_dict):
        """Classify an SFCC error."""
        prompt = f"""...[format prompt]..."""
        inputs = self.tokenizer(prompt, return_tensors="pt").to("cuda")
        outputs = self.model.generate(**inputs, max_new_tokens=16)
        return self.tokenizer.batch_decode(outputs)[0]

# Usage
classifier = CommerceExpertClassifier("llama")
error = {"code": "E_TIER_QUOTA", "message": "Quota exceeded"}
category = classifier.classify(error)  # → "Quota"

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⚠️ Limitations

• Single epoch training — Smoke test only. Production use may benefit from multi-epoch training.
• Synthetic data — Training data includes generated examples. Real-world error variations may differ.
• SFCC-specific — Domain-specific to Salesforce Commerce Cloud. Performance on generic errors may be lower.
• Language — English only. Non-English error messages not tested.
• Model selection — Choose based on deployment constraints (edge vs. accuracy vs. explainability trade-off).

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🔄 Roadmap

Version	Focus	Status
v1.0	Initial release, 9 error classes	✅ Live
v1.1	Multi-epoch training, expanded taxonomy	🔜 Planned
v2.0	Event classification, remediation suggestions	💡 Future

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📚 Reproducibility

All training code and data generation scripts are included:

• src/data/data_generator.py — Synthetic error generation
• src/data/cartridge_extractor.py — Extract from GitHub cartridges
• src/data/data_validator.py — Quality assurance
• src/data/data_pipeline.py — Full pipeline orchestration
• src/eval/eval_compare_models.py — Model evaluation

Training notebooks (Colab) available in training/notebooks/.

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📝 Citation

@software{CommerceExpert2026,
  title={CommerceExpert v1: Fine-Tuned LLMs for SFCC Error Classification},
  author={Filho, Darleison},
  year={2026},
  month={April},
  url={https://huggingface.co/darleison/sfcc-commerce-expert-v1}
}

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📄 License

Apache 2.0 — Free for research, commercial, and derivative use. See LICENSE for details.

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🙋 Support

• Issues: GitHub Issues
• Discussions: HuggingFace Discussions

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Ready to use. Fully reproducible. Open source.