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arc53 / DocsGPT

Chatbot for documentation, that allows you to chat with your data. Privately deployable, provides AI knowledge sharing and integrates knowledge into your AI workflow
https://app.docsgpt.cloud/
MIT License
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build(deps): bump transformers from 4.44.2 to 4.46.2 in /application #1429

Open dependabot[bot] opened 1 week ago

dependabot[bot] commented 1 week ago

Bumps transformers from 4.44.2 to 4.46.2.

Release notes

Sourced from transformers's releases.

Patch release v4.46.2

Mostly had to finish the gradient accumulation ! Thanks to @​techkang and @​Ryukijano 🤗

Patch release v4.46.1

Patch release v4.4.61

This is mostly for fx and onnx issues!

** Fix regression loading dtype #34409 by @​SunMarc ** LLaVa: latency issues #34460 by @​zucchini-nlp ** Fix pix2struct #34374 by @​IlyasMoutawwakil ** Fix onnx non-exposable inplace aten op #34376 by @​IlyasMoutawwakil ** Fix torch.fx issue related to the new loss_kwargs keyword argument #34380 by @​michaelbenayoun

Release v4.46.0

New model additions

Moshi

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.

image

Zamba

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.

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