Fine-tuned bitnet LoRA adapters trained using qvac-rnd-fabric-llm-bitnet — the first truly cross-platform bitnet inference and LoRA fine-tuning framework for Large Language Models. These adapters work on any GPU (Adreno, Mali, Apple Silicon, AMD, Intel, NVIDIA) using Vulkan and Metal backends. > These adapters are domain-specific and intended for biomedical Q&A tasks only. > > The LoRA adapters were fine-tuned on PubMedQA biomedical data using a structured Q: … A: prompt format. They are not general-purpose conversational models. > > What to expect with off-topic prompts: If you provide casual or unrelated input the model will not crash, but it will produce nonsensical or hallucinated biomedical-sounding text. This is expected behavior — the adapter has shifted the model’s output distribution toward medical literature, so it will attempt to generate biomedical content regardless of the input. > > For best results: > — Use the structured format: «Q: nA:» > — Keep prompts within the biomedical/clinical domain > — Use recommended temperature settings (0.3–0.5 for factual answers) > > This model is a research artifact and must NOT be used for actual medical diagnosis, treatment…
Модальности:
Генерация текста
Задача: Генерация текста
Автор: qvac
Теги: gguf, bitnet.cpp, lora, msft, fine-tuning, biomedical, cross-platform, en
Лайков: 4 | Загрузок: 230
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
