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* toctree * not-doctested.txt * collapse sections * feedback * update * rewrite get started sections * fixes * fix * loading models * fix * customize models * share * fix link * contribute part 1 * contribute pt 2 * fix toctree * tokenization pt 1 * Add new model (#32615) * v1 - working version * fix * fix * fix * fix * rename to correct name * fix title * fixup * rename files * fix * add copied from on tests * rename to `FalconMamba` everywhere and fix bugs * fix quantization + accelerate * fix copies * add `torch.compile` support * fix tests * fix tests and add slow tests * copies on config * merge the latest changes * fix tests * add few lines about instruct * Apply suggestions from code review Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * fix * fix tests --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * "to be not" -> "not to be" (#32636) * "to be not" -> "not to be" * Update sam.md * Update trainer.py * Update modeling_utils.py * Update test_modeling_utils.py * Update test_modeling_utils.py * fix hfoption tag * tokenization pt. 2 * image processor * fix toctree * backbones * feature extractor * fix file name * processor * update not-doctested * update * make style * fix toctree * revision * make fixup * fix toctree * fix * make style * fix hfoption tag * pipeline * pipeline gradio * pipeline web server * add pipeline * fix toctree * not-doctested * prompting * llm optims * fix toctree * fixes * cache * text generation * fix * chat pipeline * chat stuff * xla * torch.compile * cpu inference * toctree * gpu inference * agents and tools * gguf/tiktoken * finetune * toctree * trainer * trainer pt 2 * optims * optimizers * accelerate * parallelism * fsdp * update * distributed cpu * hardware training * gpu training * gpu training 2 * peft * distrib debug * deepspeed 1 * deepspeed 2 * chat toctree * quant pt 1 * quant pt 2 * fix toctree * fix * fix * quant pt 3 * quant pt 4 * serialization * torchscript * scripts * tpu * review * model addition timeline * modular * more reviews * reviews * fix toctree * reviews reviews * continue reviews * more reviews * modular transformers * more review * zamba2 * fix * all frameworks * pytorch * supported model frameworks * flashattention * rm check_table * not-doctested.txt * rm check_support_list.py * feedback * updates/feedback * review * feedback * fix * update * feedback * updates * update --------- Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
8.1 KiB
RoFormer
Overview
The RoFormer model was proposed in RoFormer: Enhanced Transformer with Rotary Position Embedding by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
The abstract from the paper is the following:
Position encoding in transformer architecture provides supervision for dependency modeling between elements at different positions in the sequence. We investigate various methods to encode positional information in transformer-based language models and propose a novel implementation named Rotary Position Embedding(RoPE). The proposed RoPE encodes absolute positional information with rotation matrix and naturally incorporates explicit relative position dependency in self-attention formulation. Notably, RoPE comes with valuable properties such as flexibility of being expand to any sequence lengths, decaying inter-token dependency with increasing relative distances, and capability of equipping the linear self-attention with relative position encoding. As a result, the enhanced transformer with rotary position embedding, or RoFormer, achieves superior performance in tasks with long texts. We release the theoretical analysis along with some preliminary experiment results on Chinese data. The undergoing experiment for English benchmark will soon be updated.
This model was contributed by junnyu. The original code can be found here.
Usage tips
RoFormer is a BERT-like autoencoding model with rotary position embeddings. Rotary position embeddings have shown improved performance on classification tasks with long texts.
Resources
- Text classification task guide
- Token classification task guide
- Question answering task guide
- Causal language modeling task guide
- Masked language modeling task guide
- Multiple choice task guide
RoFormerConfig
autodoc RoFormerConfig
RoFormerTokenizer
autodoc RoFormerTokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary
RoFormerTokenizerFast
autodoc RoFormerTokenizerFast - build_inputs_with_special_tokens
RoFormerModel
autodoc RoFormerModel - forward
RoFormerForCausalLM
autodoc RoFormerForCausalLM - forward
RoFormerForMaskedLM
autodoc RoFormerForMaskedLM - forward
RoFormerForSequenceClassification
autodoc RoFormerForSequenceClassification - forward
RoFormerForMultipleChoice
autodoc RoFormerForMultipleChoice - forward
RoFormerForTokenClassification
autodoc RoFormerForTokenClassification - forward
RoFormerForQuestionAnswering
autodoc RoFormerForQuestionAnswering - forward
TFRoFormerModel
autodoc TFRoFormerModel - call
TFRoFormerForMaskedLM
autodoc TFRoFormerForMaskedLM - call
TFRoFormerForCausalLM
autodoc TFRoFormerForCausalLM - call
TFRoFormerForSequenceClassification
autodoc TFRoFormerForSequenceClassification - call
TFRoFormerForMultipleChoice
autodoc TFRoFormerForMultipleChoice - call
TFRoFormerForTokenClassification
autodoc TFRoFormerForTokenClassification - call
TFRoFormerForQuestionAnswering
autodoc TFRoFormerForQuestionAnswering - call
FlaxRoFormerModel
autodoc FlaxRoFormerModel - call
FlaxRoFormerForMaskedLM
autodoc FlaxRoFormerForMaskedLM - call
FlaxRoFormerForSequenceClassification
autodoc FlaxRoFormerForSequenceClassification - call
FlaxRoFormerForMultipleChoice
autodoc FlaxRoFormerForMultipleChoice - call
FlaxRoFormerForTokenClassification
autodoc FlaxRoFormerForTokenClassification - call
FlaxRoFormerForQuestionAnswering
autodoc FlaxRoFormerForQuestionAnswering - call