Bump transformers from 4.36.0 to 4.39.2

[dependabot]

Bumps transformers from 4.36.0 to 4.39.2.

Release notes

Sourced from transformers's releases.

Patch release v4.39.2

Series of fixes for backwards compatibility (AutoAWQ and other quantization libraries, imports from trainer_pt_utils) and functionality (LLaMA tokenizer conversion)

• Safe import of LRScheduler #29919
• [BC] Fix BC for other libraries #29934
• [LlamaSlowConverter] Slow to Fast better support #29797

Patch release v4.39.1

Patch release to fix some breaking changes to LLaVA model, fixes/cleanup for Cohere & Gemma and broken doctest

• Correct llava mask & fix missing setter for vocab_size #29389
• [cleanup] vestiges of causal mask #29806
• [SuperPoint] Fix doc example (huggingface/transformers#29816)

Release v4.39.0

v4.39.0

🚨 VRAM consumption 🚨

The Llama, Cohere and the Gemma model both no longer cache the triangular causal mask unless static cache is used. This was reverted by #29753, which fixes the BC issues w.r.t speed , and memory consumption, while still supporting compile and static cache. Small note, fx is not supported for both models, a patch will be brought very soon!

New model addition

Cohere open-source model

Command-R is a generative model optimized for long context tasks such as retrieval augmented generation (RAG) and using external APIs and tools. It is designed to work in concert with Cohere's industry-leading Embed and Rerank models to provide best-in-class integration for RAG applications and excel at enterprise use cases. As a model built for companies to implement at scale, Command-R boasts:

• Strong accuracy on RAG and Tool Use
• Low latency, and high throughput
• Longer 128k context and lower pricing
• Strong capabilities across 10 key languages
• Model weights available on HuggingFace for research and evaluation

• Cohere Model Release by @​saurabhdash2512 in #29622

LLaVA-NeXT (llava v1.6)

Llava next is the next version of Llava, which includes better support for non padded images, improved reasoning, OCR, and world knowledge. LLaVA-NeXT even exceeds Gemini Pro on several benchmarks.

Compared with LLaVA-1.5, LLaVA-NeXT has several improvements:

• Increasing the input image resolution to 4x more pixels. This allows it to grasp more visual details. It supports three aspect ratios, up to 672x672, 336x1344, 1344x336 resolution.
• Better visual reasoning and OCR capability with an improved visual instruction tuning data mixture.
• Better visual conversation for more scenarios, covering different applications.
• Better world knowledge and logical reasoning.
• Along with performance improvements, LLaVA-NeXT maintains the minimalist design and data efficiency of LLaVA-1.5. It re-uses the pretrained connector of LLaVA-1.5, and still uses less than 1M visual instruction tuning samples. The largest 34B variant finishes training in ~1 day with 32 A100s.*

LLaVa-NeXT incorporates a higher input resolution by encoding various patches of the input image. Taken from the original paper.

MusicGen Melody

... (truncated)

Commits

• 97c00cd Release: v4.39.2
• e40fe39 [LlamaSlowConverter] Slow to Fast better support (#29797)
• 02b1012 [BC] Fix BC for other libraries (#29934)
• 1b6d501 Safe import of LRScheduler (#29919)
• cbe58b4 Release: v4.39.1
• 056df1d [SuperPoint] Fix doc example (#29816)
• e49ebae [cleanup] vestiges of causal mask (#29806)
• dc8b789 Correct llava mask & fix missing setter for vocab_size (#29389)
• f4364a6 style post patch
• a2a9516 path llava-next
• Additional commits viewable in compare view

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[dependabot]

Superseded by #187.