A Mistral-7B-v0.1 fine-tuned on query generation task. Main use cases: synthetic query generation for downstream embedding fine-tuning tasks — when you have only documents and no queries/labels. Such task can be done with the nixietune toolkit, see the nixietune.qgen.generate recipe. synthetic dataset expansion for further embedding training — when you DO have query-document pairs, but only a few. You can fine-tune the nixie-querygen-v2 on existing pairs, and then expand your document corpus with synthetic queries (which are still based on your few real ones). See nixietune.qgen.train recipe. The idea behind the approach is taken from the doqT5query model. See the original paper Rodrigo Nogueira and Jimmy Lin. From doc2query to docTTTTTquery. We used 200k query-document pairs sampled randomly from a diverse set of IR datasets: model-.safetensors: Pytorch FP16 checkpoint, suitable for down-stream fine-tuning ggml-model-f16.gguf: GGUF F16 non-quantized llama-cpp checkpoint, for CPU inference ggml-model-q4.gguf: GGUF Q4_0 quantized llama-cpp checkpoint, for fast (and less precise) CPU inference. [short|medium|long] and [question|regular] fragments are optional and can be skipped. …
Модальности:
Генерация текста
Задача: Генерация текста
Автор: nixiesearch
Теги: gguf, mistral, en, text-generation-inference, endpoints_compatible
Лайков: 3 | Загрузок: 126
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
