Releasesby GlyphTransformers Patch release v5.13.1
Patch release v5.13.1 This patch is focused on enabling transformers for the latest release of vllm! Be more defensive with remap_legacy_layer_types for custom models ( #47245 ) from @hmellor Fix custom code which doesn't know about the new linear layer type names ( #47174 ) from @hmellor Fix case where _LazyAutoMapping.register is passed a str key ( #47148...
Also shipped Jul 11, 2026Hugging Face in July 2026 →
Seedream 5.0 Pro is now available on AI Gateway
Seedream 5.0 Pro is now available on AI Gateway . Seedream 5.0 Pro is an image generation and editing model. It generates images from text, rendering text without spelling errors and following typographic rules, and produces dense infographics with charts, timelines, and layouts alongside realistic imagery.
Resend Connector for Claude
Connect Resend to Claude in one click from the connector directory.
arize-phoenix: v17.26.0
17.26.0 (2026-07-11) Features clarify forced tool choice menus (ef40f5e) Bug Fixes make span status code filters case insensitive (d6b3740) ui: add selected spans to newly created dataset (5da578b) ui: constrain action-bar dataset picker height (b0e718b)
arize-phoenix: v17.25.0
17.25.0 (2026-07-11) Features agents: approval-gated annotation config tools + span-coding for PXI (189271b) phoenix env file (c0ab6a9) ui: add Metrics tab to the dataset page with experiment charts (2c00ee5) ui: refine experiment metric charts (f1eb3b8) Bug Fixes ui: keep filters applied on project tables during live streaming refresh (fd6ce19) ui: make...
unstructured 0.24.1
- fix: sanitize v2 HTML output to prevent stored XSS (GHSA-v5mq-3xhg-98m9)
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Transformers Release v5.14.0
Release v5.14.0 New Model additions Inkling (fresh from Thinking Machines): 975B total, 41B active Add Inkling model #47347 by @molbap @Cyrilvallez @eustlb and @zucchini-nlp Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs.
Transformers Patch release: v5.14.1
Patch release v5.14.1 This patch solves a few issues which appeared when integrating Inkling model, most notably an issue affecting models using EncoderDecoderCache during assisted generation. It also fixes an issue that could appear during prefill with StaticCache and sdpa without padding for Inkling which uses a position_bias.
Transformers Release v5.13.0
Release v5.13.0 New Model additions KimiK 2.5, 2.6, and 2.7 This release includes the architecture for Kimi 2.5 which is used by 2.5-2.7: Kimi K2.5 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration.
Transformers Release: v5.15.0
Release v5.15.0 New Model additions Meta Muse Glimmer Muse Glimmer, released today, is Meta’s new multimodal model, especially designed for agentic use cases. Distilled from Muse to 30B parameters, and released under the Apache 2.0 license, it can be deployed to local setups for privacy-aware applications such as coding, document analysis, personal...
Transformers Patch release: v5.15.1
Patch release v5.15.1 This patch most notably solves a few issues with DFlash and MTP candidate generators, as well as an issue where images could sometimes not be processed on accelerator if using Lanczos filter.
Transformers Release v5.16.1
Release v5.16.1 This is a special release as we include GLM! (and a few small fixes) GLM-5.3-Flash GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series.
Transformers Release: v5.16.0
Release v5.16.0 New Model additions Qwen4-Exp Qwen4-Exp builds on Qwen3.5's hybrid text and multimodal architecture with three key components: GatedResidual (GR), Qwen Sparse Attention (QSA), and Per-Layer Embedding (PLE). GR is a Qwen-developed residual architecture that combines Hyper-Connection with GatedNorm.
Transformers Release 5.17.0
Release v5.17.0 New Model additions HYV4 Hy4-Preview is a 780B-parameter mixture-of-experts language model that activates 49B parameters per token. Each MoE layer holds 256 routed experts plus one always-active shared expert and routes every token to 8 of them. The context window is 1M tokens.