T3 stands for Terribly Tiny Transformers that are an efficient way of creating tiny distilled (student) models for hallucination-free LLM models in parameter-constrained environment (edge devices).
The base model is a T3 adaptation of T5 model. The paradigm of T3 models can be extended to all types of models ( encoder only, decoder only & seq2seq)
{"language": ["en"], "license": "apache-2.0", "datasets": ["McGill-NLP/FaithDial"], "metrics": ["bleu", "bertscore", "accuracy"], "pipeline_tag": "conversational"}
transformers pytorch t5 text2text-generation conversational en dataset:McGill-NLP/FaithDial license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #conversational #en #dataset-McGill-NLP/FaithDial #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

T3 stands for Terribly Tiny Transformers that are an efficient way of creating tiny distilled (student) models for hallucination-free LLM models in parameter-constrained environment (edge devices).
The base model is a T3 adaptation of T5 model. The paradigm of T3 models can be extended to all types of models ( encoder only, decoder only & seq2seq)
TAGS
#transformers #pytorch #t5 #text2text-generation #conversational #en #dataset-McGill-NLP/FaithDial #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

TAGS
#transformers #pytorch #t5 #text2text-generation #conversational #en #dataset-McGill-NLP/FaithDial #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
