I have a list and I want to convert it to a huggingface dataset for training model, I follow some tips and here is my code, from datasets import Dataset class MkqaChineseDataset(Dataset): def __init__(self, data): # super().__init__() if add this, it shows super().__init_...
I want to create a new hugging face (HF) architecture with some existing tokenizer (any one that is excellent is fine). Let's say decoder to make it concrete (but both is better). How does one do this? I found thishttps://huggingface.co/docs/transformers/create_a_m...
HuggingFace: ID is "local:<model_name>" e.g. "local:BAAI/bge-small-en" Embeddings: Supports text-embedding-ada-002 by default, but also supports Hugging Face models. To use a hugging face model simply prepend with local, e.g. local:BAAI/bge-small-en. Issues / Contributions Running int...
Huggingface homepage: https://huggingface.co/datasets/haonan-li/cmmlu """ import os from lm_eval.base import MultipleChoiceTask, rf _CITATION = """ @misc{li2023cmmlu, title={CMMLU: Measuring massive multitask language understanding in Chinese}, author={Haonan Li and Yixuan Zhang and Fajri...
One such model was the HuggingFace Vintedois Diffusion V0 1 model by creator 22h, also available on Replicate. By fine-tuning Stable Diffusion on a diverse dataset, 22h developed a versatile general-purpose model skilled at detailed image generation. Around the same period, two checkpoint models...
Transition to the fine-tuning of LLMs and learn how to obtain a suitable dataset and fine-tune the model to improve its performance on specific tasks. Explore the future of LLMs and their potential applications, as well as career opportunities in this exciting field. ...
Edinburgh 56 speaker dataset:https://datashare.is.ed.ac.uk/handle/10283/2791; License:https://datashare.is.ed.ac.uk/bitstream/handle/10283/2791/license_text?sequence=11&isAllowed=y VocalSet: A Singing Voice Datasethttps://zenodo.org/record/1193957#.X1hkxYtlCHs; License: Creative Commons At...
Get started customizing your language model using NeMo This post walked through the process of customizing LLMs for specific use cases using NeMo and techniques such as prompt learning. From a single public checkpoint, these models can be adapted to numerous NLP applications through a parameter-effi...
HuggingFace or ModelScope repoSource: pulls a model or dataset from the ModelScope or HuggingFace community. Valid values: ModelScope/Model, ModelScope/DataSet, HuggingFace/Model, and HuggingFace/DataSet. repoId: the ID of the model or dataset. revision: the version. Default value: main or ...
dataset = load_dataset(huggingface_dataset_name, split="train") # LoRA attention dimension lora_r = 64 # Alpha parameter for LoRA scaling lora_alpha = 16 # Dropout probability for LoRA layers lora_dropout = 0.1 # Load LoRA configuration ...