base_model.model.transformer.encoder.layers.0.self_attention.query_key_value.lora_A.default.weight: shape = [8, 4096] sum = 1.0851411819458008 base_model.model.transformer.encoder.layers.0.self_attention.query_key_value.lora_B.default.weight: shape = [4608, 8] sum = 0.0 base_model.model...
llm_int8_threshold=6.0,llm_int8_has_fp16_weight=False,)tokenizer=AutoTokenizer.from_pretrained(model_name_or_path,trust_remote_code=True)# cache_dir='./'缓存到当前工作路径 model=AutoModel.from_pretrained(model_name_or_path,quantization_config=bnb_config,trust_remote_code=True)# cache_dir='...
具体用法见https://huggingface.co/docs/datasets/index。 train_dict=convert_txt('/kaggle/input/wechatdata/train.txt')train_data=Dataset.from_dict(train_dict)val_dict=convert_txt('/kaggle/input/wechatdata/val.txt')val_data=Dataset.from_dict(val_dict) 我们需要定义preprocess函数来处理数据集,其功...
base_model.model.transformer.encoder.layers.0.self_attention.query_key_value.lora_A.default.weight: shape = [8, 4096] sum = 1.0851411819458008 base_model.model.transformer.encoder.layers.0.self_attention.query_key_value.lora_B.default.weight: shape = [4608, 8] sum = 0.0 base_model.model....
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"Transformers from Scratch" by Peter Bloem: An in-depth tutorial series that explains the Transformer model from scratch, without using any existing libraries. It includes code examples in Python and PyTorch. Link: https://peterbloem.nl/blog/transformers "The Annotated Transformer" by Harvard NLP...
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I'll plan to learn more and include some code examples related to Transformer++ in an upcoming blog post. In the meantime, if you're interested in understanding transformers better, I highly recommend checking out Andrej Karpathy’s video 'Let’s Build GPT: from scratch, in code, spelled ...
GRU is the simpler and faster version of LSTM. The choice of GRU or LSTM depends on the complexity of the use case. After RNN and LSTM, you can follow this path in order: Bi-LSTM, Encoder-Decoder, Attention, Transformer.The Devastator Posted 2 years ago arrow_drop_up1more_vert Transf...
To add to the existing comments: apart from the benefits already mentioned, a huge reason for the popularity of transformers is the ability to fine-tune a transformer for a specific task. Compared to RNNs, transformers require much more data to train from scratch, but this is mitigated by ...