The Moshi model was proposed in Moshi: a speech-text foundation model for real-time dialogue by Alexandre Défossez,
Laurent Mazaré, Manu Orsini, Amélie Royer, Patrick Pérez, Hervé Jégou, Edouard Grave and Neil Zeghidour.
Moshi is a speech-text foundation model that casts spoken dialogue as speech-to-speech generation. Starting from a
text language model backbone, Moshi generates speech as tokens from the residual quantizer of a neural audio codec,
while modeling separately its own speech and that of the user into parallel streams. This allows for the removal of
explicit speaker turns, and the modeling of arbitrary conversational dynamics. Moshi also predicts time-aligned text
tokens as a prefix to audio tokens. This “Inner Monologue” method significantly improves the linguistic quality of
generated speech and provides streaming speech recognition and text-to-speech. As a result, Moshi is the first
real-time full-duplex spoken large language model, with a theoretical latency of 160ms, 200ms in practice.
Zamba-7B-v1 is a hybrid between state-space models (Specifically Mamba) and transformer, and was trained using
next-token prediction. Zamba uses a shared transformer layer after every 6 mamba blocks. It uses the Mistral
v0.1 tokenizer. We came to this architecture after a series of ablations at small scales. Zamba-7B-v1 was
pre-trained on 1T tokens of text and code data.
The GLM Model was proposed in ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools by GLM Team,
THUDM & ZhipuAI.
The abstract from the paper starts with the following:
We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This
report primarily focuses on the GLM-4 language series, which includes GLM-4, GLM-4-Air, and GLM-4-9B.
The Idefics3 model was proposed in Building and better understanding vision-language models: insights and future directions by Hugo Laurençon, Andrés Marafioti, Victor Sanh, and Léo Tronchon.
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Updates the requirements on transformers to permit the latest version.
Release notes
Sourced from transformers's releases.
... (truncated)
Commits
c2820c9
fix list 3.8b298161
v4.46.0b0f0c61
Add SynthID (watermerking by Google DeepMind) (#34350)e50bf61
Fix red CI: benchmark script (#34351)c42b322
skiptest_pipeline_depth_estimation
temporarily (#34316)d9f7336
Enable Gradient Accumulation fix across all models + trainer fully in forward...1fb575f
Support boolean tool args (#34208)343c8cb
Added Deberta model type support (#34308)5ba85de
[docs] Fix Korean toctree (#34324)049682a
Example doc for token classification of Llama and Dependent/Copied Models (#3...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
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📚 Documentation preview 📚: https://torchmetrics--2807.org.readthedocs.build/en/2807/