06f1fd69a6
3.7 KiB
This model was published in HF papers on 2021-01-11 and contributed to Hugging Face Transformers on 2022-11-15.
Switch Transformers
Switch Transformers is a sparse T5 model where the MLP layer is replaced by a Mixture-of-Experts (MoE). A routing mechanism associates each token with an expert and each expert is a dense MLP. Sparsity enables better scaling and the routing mechanism allows the model to select relevant weights on the fly which increases model capacity.
You can find all the original Switch Transformers checkpoints under the Switch Transformer collection.
Tip
This model was contributed by ybelkada and ArthurZ.
Click on the Switch Transformers models in the right sidebar for more examples of how to apply Switch Transformers to different natural language tasks.
The example below demonstrates how to predict the masked token with [Pipeline], [AutoModel], and from the command line.
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
model = AutoModelForSeq2SeqLM.from_pretrained("google/switch-base-8", device_map="auto")
input_text = "The capital of France is <extra_id_0>."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.
The example below uses bitsandbytes to only quantize the weights to 8-bits.
# pip install bitsandbytes
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, BitsAndBytesConfig
tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForSeq2SeqLM.from_pretrained("google/switch-base-8", device_map="auto", quantization_config=quantization_config)
input_text = "The capital of France is <extra_id_0>."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(0)
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
SwitchTransformersConfig
autodoc SwitchTransformersConfig
SwitchTransformersTop1Router
autodoc SwitchTransformersTop1Router - forward
SwitchTransformersSparseMLP
autodoc SwitchTransformersSparseMLP - forward
SwitchTransformersModel
autodoc SwitchTransformersModel - forward
SwitchTransformersForConditionalGeneration
autodoc SwitchTransformersForConditionalGeneration - forward
SwitchTransformersEncoderModel
autodoc SwitchTransformersEncoderModel - forward