A LoRA adapter for meta-llama/Llama-3.2-3B-Instruct, fine-tuned for marketing-analytics question answering: reading values from marketing reports (spend, revenue, conversions, funnels), multi-step numeric reasoning over tables, and abstaining when a figure isn’t present. Trained with Adaption’s AutoScientist on the Marketing Spend & Revenue QA dataset, built for the Adaption AutoScientist Challenge. > Built with Llama. This is a LoRA adapter, not a full model — you load it on top of the > base meta-llama/Llama-3.2-3B-Instruct (a gated model; you must accept its license on > HuggingFace to download it). Evaluated head-to-head against the base model (AutoScientist LLM-judge win rate): A note on interpreting this honestly: win rate is measured against the base, so it reflects headroom. The same dataset trained on a much stronger base (gpt-oss-120B) scored ~50/50 — that base already handles marketing table-QA well. The lift is largest on a small base with room to learn, which is exactly the case here. Training converged cleanly: final train loss 0.194, eval loss 0.214 (3 epochs, 63 steps), with validation tracking training throughout (no overfitting). To merge the adapter into the…
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: vinod-anbalagan
Теги: peft, llama, lora, llama-3.2, marketing, marketing-analytics, instruction-tuning, table-question-answering
Лайков: 3 | Загрузок: 20
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
