Falcon is a class of causal decoder-only models built by TII. The largest Falcon checkpoints have been trained on >=1T tokens of text, with a particular emphasis on the RefinedWeb corpus. They are made available under the Apache 2.0 license.
Falcon’s architecture is modern and optimized for inference, with multi-query attention and support for efficient attention variants like FlashAttention. Both ‘base’ models trained only as causal language models as well as ‘instruct’ models that have received further fine-tuning are available.
[Falcon] Remove SDPA for falcon to support earlier versions of PyTorch (< 2.0) by @younesbelkada in #25947
Code Llama
Code Llama, is a family of large language models for code based on Llama 2, providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks.
VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.
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Updates the requirements on transformers to permit the latest version.
Release notes
Sourced from transformers's releases.
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Commits
fa6107c
modify context length for GPTQ + version bump (#25899)300d6a4
Remove Falcon from undocumented list (#26008)fa522d8
🌐[i18n-KO] Translatedllm_tutorial.md
to Korean (#25791)3e203f9
Fix small typo README.md (#25934)842e99f
TF-OPT attention mask fixes (#25238)f6301b9
Falcon: fix revision propagation (#26006)f6295c6
Update README.md (#26003)172f42c
save space when converting hf model to megatron model. (#25950)b8def68
Fix Mega chunking error when using decoder-only model (#25765)4fa0aff
[VITS
] tokenizer integration test: fix revision did not exist (#25996)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase
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