Bump transformers from 4.38.2 to 4.39.0 in /docker/ml

[dependabot]

Bumps transformers from 4.38.2 to 4.39.0.

Release notes

Sourced from transformers's releases.

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

The MusicGen Melody model was proposed in Simple and Controllable Music Generation by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez.

MusicGen Melody is a single stage auto-regressive Transformer model capable of generating high-quality music samples conditioned on text descriptions or audio prompts. The text descriptions are passed through a frozen text encoder model to obtain a sequence of hidden-state representations. MusicGen is then trained to predict discrete audio tokens, or audio codes, conditioned on these hidden-states. These audio tokens are then decoded using an audio compression model, such as EnCodec, to recover the audio waveform.

Through an efficient token interleaving pattern, MusicGen does not require a self-supervised semantic representation of the text/audio prompts, thus eliminating the need to cascade multiple models to predict a set of codebooks (e.g. hierarchically or upsampling). Instead, it is able to generate all the codebooks in a single forward pass.

• Add MusicGen Melody by @​ylacombe in #28819

PvT-v2

The PVTv2 model was proposed in PVT v2: Improved Baselines with Pyramid Vision Transformer by Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. As an improved variant of PVT, it eschews position embeddings, relying instead on positional information encoded through zero-padding and overlapping patch embeddings. This lack of reliance on position embeddings simplifies the architecture, and enables running inference at any resolution without needing to interpolate them.

• Add PvT-v2 Model by @​FoamoftheSea in #26812

... (truncated)

Commits

• f4364a6 style post patch
• a2a9516 path llava-next
• 0788481 [BC 4.37 -> 4.38] for Llama family, memory and speed (#29753)
• 74f2900 re-make pre-release for cherry picked commits
• 1a79db7 make fix copies
• 9ca7ac9 Add LLaVa-1.6, bis (#29586)
• 8049122 SuperPointModel -> SuperPointForKeypointDetection (#29757)
• 1112b24 fix jinja2 package version check (#29754)
• 04cec40 Update Mamba types and pass through use_cache attr to MambaModel (#29605)
• dd39ad4 [Tests] Remove unused code (#29737)
• Additional commits viewable in compare view

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[xsoar-bot]

Docker Image Ready - Dev

Docker automatic build at CircleCI has deployed your docker image: devdemisto/ml:1.0.0.90990
It is available now on docker hub at: https://hub.docker.com/r/devdemisto/ml/tags
Get started by pulling the image:

docker pull devdemisto/ml:1.0.0.90990

Docker Metadata

• Image Size: 1391.15 MB
• Image ID: sha256:9c346e8d983867a957f16ca1fa8b240f35b063cf82c20330dc395df81f2ce35c
• Created: 2024-03-21T14:36:48.060733288Z
• Arch: linux/amd64
• Command: ["python3"]
• Environment:
  • PATH=/usr/local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
  • LANG=C.UTF-8
  • GPG_KEY=A035C8C19219BA821ECEA86B64E628F8D684696D
  • PYTHON_VERSION=3.10.10
  • PYTHON_PIP_VERSION=22.3.1
  • PYTHON_SETUPTOOLS_VERSION=65.5.1
  • PYTHON_GET_PIP_URL=https://github.com/pypa/get-pip/raw/d5cb0afaf23b8520f1bbcfed521017b4a95f5c01/public/get-pip.py
  • PYTHON_GET_PIP_SHA256=394be00f13fa1b9aaa47e911bdb59a09c3b2986472130f30aa0bfaf7f3980637
  • DOCKER_IMAGE=devdemisto/ml:1.0.0.90990
  • TRANSFORMERS_CACHE=/ml/.cache
  • NLTK_DATA=/ml/nltk_data
  • MPLCONFIGDIR=/ml/matplotlib
• Labels:
  • org.opencontainers.image.authors:Demisto <[email protected]>
  • org.opencontainers.image.revision:665399f13083257a82f389c61c8f640e74e5d19a
  • org.opencontainers.image.version:1.0.0.90990

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Docker Image Ready - Production

Docker automatic build at CircleCI has deployed your docker image: demisto/ml:1.0.0.91000
It is available now on docker hub at: https://hub.docker.com/r/demisto/ml/tags
Get started by pulling the image:

docker pull demisto/ml:1.0.0.91000

Docker Metadata

• Image Size: 1391.15 MB
• Image ID: sha256:5e278428c76802d2f6196a92ae4b865d603ae7003b003afa3de06c5edf088b88
• Created: 2024-03-21T14:48:53.416543339Z
• Arch: linux/amd64
• Command: ["python3"]
• Environment:
  • PATH=/usr/local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
  • LANG=C.UTF-8
  • GPG_KEY=A035C8C19219BA821ECEA86B64E628F8D684696D
  • PYTHON_VERSION=3.10.10
  • PYTHON_PIP_VERSION=22.3.1
  • PYTHON_SETUPTOOLS_VERSION=65.5.1
  • PYTHON_GET_PIP_URL=https://github.com/pypa/get-pip/raw/d5cb0afaf23b8520f1bbcfed521017b4a95f5c01/public/get-pip.py
  • PYTHON_GET_PIP_SHA256=394be00f13fa1b9aaa47e911bdb59a09c3b2986472130f30aa0bfaf7f3980637
  • DOCKER_IMAGE=demisto/ml:1.0.0.91000
  • TRANSFORMERS_CACHE=/ml/.cache
  • NLTK_DATA=/ml/nltk_data
  • MPLCONFIGDIR=/ml/matplotlib
• Labels:
  • org.opencontainers.image.authors:Demisto <[email protected]>
  • org.opencontainers.image.revision:4b8ae5880b13d1f5b0d066a4ca1575f6841ee080
  • org.opencontainers.image.version:1.0.0.91000