This model was trained using H2O LLM Studio. — Base model: EleutherAI/pythia-70m-deduped-v0 Once you’ve defined the function, you can proceed to set up the prompt and the model To use the model with the transformers library on a machine with GPUs, first make sure you have the transformers library installed. Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. — Either leave token=True in the pipeline and login to hugginface_hub by running You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: Alternatively, you can download h2oaipipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the transformers package, this will allow you to set trustremotecode=False`. You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: You can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting . This model was trained using H2O LLM Studio and with the configuration…
Модальности:
Генерация текста
Задача: Генерация текста
Автор: diegomiranda
Теги: gpt_neox, gpt, llm, large language model, h2o-llmstudio, en, text-generation-inference
Лайков: 3 | Загрузок: 24
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
