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Bryan-Roe / semantic-kernel

Integrate cutting-edge LLM technology quickly and easily into your apps
https://bryan-roe.github.io/semantic-kernel/
Apache License 2.0
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Bumps [transformers](https://github.com/huggingface/transformers) from 4.36.1 to 4.39.3. #351

Open Bryan-Roe opened 3 months ago

Bryan-Roe commented 3 months ago

Bumps transformers from 4.36.1 to 4.39.3.

Release notes

Sourced from transformers's releases.

Release v4.39.3

The AWQ issue persisted, and there was a regression reported with beam search and input embeddings.

Changes

  • Fix BC for AWQ quant #29965
  • generate fix breaking change for patch #29976

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

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

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.*

... (truncated)

Commits


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Originally posted by @dependabot in https://github.com/Bryan-Roe/semantic-kernel/pull/52

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