pricing-specialist

Model description

• Model Type: Fine-tuned LLM for structured price estimation
• Architecture: Adapted from OpenAI GPT and trained on curated product datasets
• Primary Use: Predicting item prices from text-based descriptions
• Integration: Deployed remotely through Modal for scalable inference
• Experiment Tracking: Managed via Weights & Biases
• Owner Project: Pricely

Intended uses & limitations

Intended uses

• Estimating product prices from e-commerce deal descriptions
• Supporting multi-agent workflows for deal discovery and ranking
• Demonstrating fine-tuned regression-like behavior in large language models

Limitations

• Predictions are approximate and may not reflect real-world prices
• Model performance decreases with ambiguous or incomplete inputs
• Primarily intended for research and demonstration, not production valuation

Training and evaluation data

• Dataset: jordanmatsumoto/pricing_data
• Size: ~350,000 structured product entries (title, description, true price)
• Coverage: Balanced across electronics, appliances, home, and consumer goods
• Preparation: Cleaned and standardized for fine-tuning

Training procedure

Training hyperparameters

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 1
• seed: 42
• optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 0.25

Framework versions

• PEFT 0.14.0
• Transformers 4.48.3
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.4