A domain-tuned reasoning merged model for HIT Promotional Products. It answers the questions a sales rep or distributor asks while quoting a job: what will this run at 2,500 pieces, how big is the imprint area, can we hit a 14-day deadline, does this qualify as a less-than-minimum order. > What the prices are. Catalog figures are HIT’s NET/EQP pricing — what a distributor pays HIT. The distributor’s own sell price, margin and freight are not in this data, and the model says so when it quotes. Validation loss fell at every checkpoint, and the best checkpoint is the final one — the model was still improving when the epoch ended, with no sign of overfitting: 0.182 -> 0.128 over 9 checkpoints, with token accuracy rising to 96.5%. Measured on the full held-out validation split at every checkpoint. A QLoRA fine-tune of microsoft/Phi-4-mini-reasoning, trained on HIT’s product feed and knowledge base. The base model is open-weights, American-built, and reasons before it answers; the adapter teaches it this catalog and HIT’s order policy, not general promotional-products trivia. Internal support for HIT sales reps and distributors, with catalog records supplied in the prompt. The…
Модальности:
Генерация текста
Области применения:
Логика и рассуждение Диалог / чат
Задача: Генерация текста
Автор: raj-hitpromo
Теги: phi3, merged, promotional-products, catalog, reasoning, hit-promotional-products, conversational, text-generation-inference
Лайков: 3 | Загрузок: 114
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
