This model serves as a demonstration of how fine-tuning foundational models using the Neo4j-Text2Cypher(2024) Dataset (link) can enhance performance on the Text2Cypher task. Please note, this is part of ongoing research and exploration, aimed at highlighting the dataset’s potential rather than a production-ready solution. Base model: google/gemma-2-9b-it Dataset: neo4j/text2cypher-2024v1 An overview of the finetuned models and benchmarking results are shared at Link1 and Link2 We need to be cautious about a few risks: In our evaluation setup, the training and test sets come from the same data distribution (sampled from a larger dataset). If the data distribution changes, the results may not follow the same pattern. The datasets used were gathered from publicly available sources. Over time, foundational models may access both the training and test sets, potentially achieving similar or even better results. 1 x A100 PCIe 31 vCPU 117 GB RAM runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04 On-Demand — Secure Cloud 60 GB Disk 60 GB Pod Volume loraconfig = LoraConfig( r=64, loraalpha=64, targetmodules=targetmodules, loradropout=0.05, bias=»none», tasktype=»CAUSALLM», )…
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: DavidLanz
Теги: gguf, conversational, neo4j, cypher, text2cypher, text2text-generation, en, endpoints_compatible
Лайков: 4 | Загрузок: 0
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
