🗓️ Weekly GitHub Activity
🦙 llama.cpp
└ Release: b10107 → b10229
└ 122 commits
- Added DSpark speculative decoding support on top of DFlash (#25173) and DeepSeek V4 MTP integration (#25784).
- Introduced support for Model Context Protocol (MCP) stdio transport in server (#26062).
- Added NextN/MTP speculative decoding support for GLM-5.2 (#25980).
- Added model support for MiniMax-M3 text and vision (#24908, #25113), GLM 5.2 / GLM-5.2-Vision (#25407, #26126), Nanbeige 4.2 (#25994), MiMo-V2.5 audio (#26190), Nemotron 3 Nano Omni (#22520), and Laguna-S-2.1 (#26233).
- CUDA backend added Q2_0 quantization support (#25707) and chunked SSD matmul for Mamba-2 prefill acceleration (#22675).
- SYCL backend added oneMKL GEMM flash attention for XMX acceleration (#25025) and RMS_NORM + MUL fusion (#26015).
- OpenCL backend now caches compiled binaries to disk (#26050).
- Added specialized chat parsers for MiniMax M3 (#26210) and Qwen3 (#26252).
- Fixed SYCL oneDNN flash attention scale memory corruption on long contexts (#25880).
- Fixed Metal memory leak when freeing models without GPU operations (#26082).
🔗 All changes | Latest release
🎨 stable-diffusion.cpp
└ Release: master-795-87a0177 → master-810-db99efd
└ 15 commits
- Added IP-Adapter Plus support with Resampler image projection (#1839)
- Exposed IP-Adapter parameters in server request schema and capabilities (#1824)
- Added support for Kroma-v0.1 LoRA models (#1842)
- Added linear multi-step sampling method (#1843)
- Allowed customizable alpha and beta parameters for the beta scheduler (#1834)
🔗 All changes | Latest release
🎵 audio.cpp
└ Release: release-0.4.2 → release-0.5
└ 74 commits
- Release 0.5 introducing new audio framework modules and model migrations (3178daf)
- Added AMD ROCm/HIP backend support for AMD GPU acceleration on Linux and Windows (#48, #153)
- Added live PCM audio streaming via CLI stdin and a live HTTP transcription endpoint (#118, #144)
- Added NVIDIA Parakeet-TDT 0.6B v3 ASR model (#111)
- Added Kroko Zipformer2 RNN-T ASR model with offline and stateful streaming support (#122)
- Added Fun-ASR-Nano offline ASR model (#155)
- Added Inflect Micro and Nano v2 TTS models (#125)
- Added BS-RoFormer source separation model (#114)
- Added Confucius4-TTS, DramaBox, and RVC models (#129)
- Added Qwen3 ASR streaming path (7ce769e)
- Accelerated Metal 1D transpose convolution execution for faster audio VAE decoding (#149)
🔗 All changes | Latest release
🦙 llama.cpp
└ Release: b10107 → b10229
└ 122 commits
- Added DSpark speculative decoding support on top of DFlash (#25173) and DeepSeek V4 MTP integration (#25784).
- Introduced support for Model Context Protocol (MCP) stdio transport in server (#26062).
- Added NextN/MTP speculative decoding support for GLM-5.2 (#25980).
- Added model support for MiniMax-M3 text and vision (#24908, #25113), GLM 5.2 / GLM-5.2-Vision (#25407, #26126), Nanbeige 4.2 (#25994), MiMo-V2.5 audio (#26190), Nemotron 3 Nano Omni (#22520), and Laguna-S-2.1 (#26233).
- CUDA backend added Q2_0 quantization support (#25707) and chunked SSD matmul for Mamba-2 prefill acceleration (#22675).
- SYCL backend added oneMKL GEMM flash attention for XMX acceleration (#25025) and RMS_NORM + MUL fusion (#26015).
- OpenCL backend now caches compiled binaries to disk (#26050).
- Added specialized chat parsers for MiniMax M3 (#26210) and Qwen3 (#26252).
- Fixed SYCL oneDNN flash attention scale memory corruption on long contexts (#25880).
- Fixed Metal memory leak when freeing models without GPU operations (#26082).
🔗 All changes | Latest release
🎨 stable-diffusion.cpp
└ Release: master-795-87a0177 → master-810-db99efd
└ 15 commits
- Added IP-Adapter Plus support with Resampler image projection (#1839)
- Exposed IP-Adapter parameters in server request schema and capabilities (#1824)
- Added support for Kroma-v0.1 LoRA models (#1842)
- Added linear multi-step sampling method (#1843)
- Allowed customizable alpha and beta parameters for the beta scheduler (#1834)
🔗 All changes | Latest release
🎵 audio.cpp
└ Release: release-0.4.2 → release-0.5
└ 74 commits
- Release 0.5 introducing new audio framework modules and model migrations (3178daf)
- Added AMD ROCm/HIP backend support for AMD GPU acceleration on Linux and Windows (#48, #153)
- Added live PCM audio streaming via CLI stdin and a live HTTP transcription endpoint (#118, #144)
- Added NVIDIA Parakeet-TDT 0.6B v3 ASR model (#111)
- Added Kroko Zipformer2 RNN-T ASR model with offline and stateful streaming support (#122)
- Added Fun-ASR-Nano offline ASR model (#155)
- Added Inflect Micro and Nano v2 TTS models (#125)
- Added BS-RoFormer source separation model (#114)
- Added Confucius4-TTS, DramaBox, and RVC models (#129)
- Added Qwen3 ASR streaming path (7ce769e)
- Accelerated Metal 1D transpose convolution execution for faster audio VAE decoding (#149)
🔗 All changes | Latest release
GitHub
spec: add DSpark speculative decoding by wjinxu · Pull Request #25173 · ggml-org/llama.cpp
This PR adds DSpark speculative decoding, layered on the merged DFlash drafter. DSpark (DeepSeek + PKU, 2026 — "Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generatio...
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A rank 256 style LoRA adapter and weight delta model for Krea 2
♡118 EschaLabs/Qwen3.6-35B-A3B-Escha-W2
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♡63 FermionResearch/Neutrino-8B
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♡62 KRAFTON/A.X-K2-Raon-Speech-21B-A3B —
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♡36 harrrshall/BarunLM-35M
A 35M parameter decoder-only base LLM designed for efficient local text generation and compact language model research.
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A collection of image-editing LoRAs that converts character images into multi-view reference sheets for character design.
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♡21 OpenMOSS-Team/OmniVAE
An audio-video VAE and text-to-audio-video generation model designed for cross-modal aligned joint generation.
♡21 OrionLLM/GRM-3.2-Sky
A multimodal LLM optimized for long-horizon agentic workflows, coding, and mathematical reasoning
♡18 ProCreations/grug-3b
A 3B parameter reasoning LLM featuring caveman-style token-efficient reasoning.
♡18 openpangu/openPangu-2.0-Pro
A 505B-A18B parameter MoE LLM from Huawei supporting 512k context, trained on Huawei Ascend.
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A 768-dimensional Korean fiction style embedding model based on Gemma 300M, designed for style similarity and authorship analysis.
♡16 Aratako/Irodori-TTS-v4-Small
A Japanese Flow Diffusion Text-to-Speech model with zero-shot style-controlled voice cloning and text-based voice design.
huggingface.co
thinkingmachines/Inkling-Small · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
🆕 [HF Models] internlm - Intern-S2-Preview-397B
https://huggingface.co/internlm/Intern-S2-Preview-397B
🔓 [HF Models] internlm - JanusCoder-14B
https://huggingface.co/internlm/JanusCoder-14B
https://huggingface.co/internlm/Intern-S2-Preview-397B
🔓 [HF Models] internlm - JanusCoder-14B
https://huggingface.co/internlm/JanusCoder-14B
huggingface.co
internlm/Intern-S2-Preview-397B · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
🔓 [HF Models] swiss-ai - wavtokenizer-large-unify-40token
https://huggingface.co/swiss-ai/wavtokenizer-large-unify-40token
https://huggingface.co/swiss-ai/wavtokenizer-large-unify-40token
huggingface.co
swiss-ai/wavtokenizer-large-unify-40token · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
📰 NVIDIA - NVIDIA Vera Storage Benchmarks: Faster Encryption, Compression, Integrity Checking, and Recovery for AI-Native Storage
Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data…
https://developer.nvidia.com/blog/nvidia-vera-storage-benchmarks-faster-encryption-compression-integrity-checking-and-recovery-for-ai-native-storage/
Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data…
https://developer.nvidia.com/blog/nvidia-vera-storage-benchmarks-faster-encryption-compression-integrity-checking-and-recovery-for-ai-native-storage/
NVIDIA Technical Blog
NVIDIA Vera Storage Benchmarks: Faster Encryption, Compression, Integrity Checking, and Recovery for AI-Native Storage
Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data, execute tools, and generate new results…
📰 HuggingFace - Deploy local agents everywhere with LFM2.5-2.6B
https://huggingface.co/blog/LiquidAI/lfm2-5-2-6b
https://huggingface.co/blog/LiquidAI/lfm2-5-2-6b
huggingface.co
Deploy local agents everywhere with LFM2.5-2.6B
A Blog post by Liquid AI on Hugging Face
🆕 [HF Models] LiquidAI - LFM2.5-2.6B-GGUF
https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF
🆕 [HF Models] LiquidAI - LFM2.5-2.6B-Base
https://huggingface.co/LiquidAI/LFM2.5-2.6B-Base
🆕 [HF Models] LiquidAI - LFM2.5-2.6B
https://huggingface.co/LiquidAI/LFM2.5-2.6B
https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF
🆕 [HF Models] LiquidAI - LFM2.5-2.6B-Base
https://huggingface.co/LiquidAI/LFM2.5-2.6B-Base
🆕 [HF Models] LiquidAI - LFM2.5-2.6B
https://huggingface.co/LiquidAI/LFM2.5-2.6B
huggingface.co
LiquidAI/LFM2.5-2.6B-Base · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
📰 Mistral - Introducing Shieldstral.
Shieldstral introduces a 3B open-weights multimodal safety classifier that outperforms models up to 7x its size.
https://mistral.ai/news/shieldstral/
Shieldstral introduces a 3B open-weights multimodal safety classifier that outperforms models up to 7x its size.
https://mistral.ai/news/shieldstral/
Mistral AI
Introducing Shieldstral. | Mistral AI
Shieldstral introduces a 3B open-weights multimodal safety classifier that outperforms models up to 7x its size.
📰 Google DeepMind - The latest AI news we announced in July 2026
https://blog.google/innovation-and-ai/technology/ai/google-ai-updates-july-2026/
https://blog.google/innovation-and-ai/technology/ai/google-ai-updates-july-2026/
Google
The latest AI news we announced in July 2026
Here are Google’s latest AI updates from July 2026
📰 Google AI Blog - A unified API for AI model routing
Google Cloud API Gateway now offers a model routing feature in Public Preview, allowing developers to dynamically route traffic to models like Gemini, Claude, or OpenAI OSS-GPT without hardcoding endpoints or managing open-source proxies. Developers can easily configure these routing rules directly within their OpenAPI 3.x specifications by mapping virtual model names to specific backend targets on a shared host. Once deployed, the Gateway acts as a serverless ingress layer that accepts standard OpenAI-compatible requests, automatically transcodes the payload to the native schema of the target model, and routes the traffic on the fly.
https://developers.googleblog.com/en/a-unified-api-for-ai-model-routing/
Google Cloud API Gateway now offers a model routing feature in Public Preview, allowing developers to dynamically route traffic to models like Gemini, Claude, or OpenAI OSS-GPT without hardcoding endpoints or managing open-source proxies. Developers can easily configure these routing rules directly within their OpenAPI 3.x specifications by mapping virtual model names to specific backend targets on a shared host. Once deployed, the Gateway acts as a serverless ingress layer that accepts standard OpenAI-compatible requests, automatically transcodes the payload to the native schema of the target model, and routes the traffic on the fly.
https://developers.googleblog.com/en/a-unified-api-for-ai-model-routing/
Googleblog
Google for Developers Blog - News about Web, Mobile, AI and Cloud
Discover how developers can configure Google Cloud API Gateway to dynamically route OpenAI-compatible requests without managing open-source proxies.
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https://lmsys.org/blog/2026-08-04-specforge-v0-3
https://lmsys.org/blog/2026-08-04-specforge-v0-3
www.lmsys.org
SpecForge v0.3.0: a Unified Disaggregated and Colocated Speculative Decoding Stack, and New Open SpecBundle Draft Models
When we first released SpecForge, a training job owned both the frozen target model and the draft model being optimized. This made EAGLE3 draft-model training practical and directly compatible with SG...
📰 Anthropic - Mariano-Florentino (Tino) Cuéllar to join Anthropic as Chief Global Affairs Officer
https://www.anthropic.com/news/tino-cuellar
https://www.anthropic.com/news/tino-cuellar
Anthropic
Mariano-Florentino (Tino) Cuéllar to join Anthropic as Chief Global Affairs Officer
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
📰 OpenAI - New ways to learn and teach with ChatGPT Work and Codex
Explore new education plugins for ChatGPT Work and Codex that help K–12 teachers, college educators, and students learn, teach, research, and build.
https://openai.com/index/learn-teach-chatgpt-work-codex
📰 OpenAI - Apple is getting this wrong
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https://openai.com/index/apple-is-getting-this-wrong
📰 OpenAI - How we built a realtime system for responsive voice AI in six months
GPT-Live enables continuous voice interaction with AI, using a turnless speech model and low-latency architecture for faster, more natural conversations.
https://openai.com/index/continuous-voice-interaction-with-gpt-live
Explore new education plugins for ChatGPT Work and Codex that help K–12 teachers, college educators, and students learn, teach, research, and build.
https://openai.com/index/learn-teach-chatgpt-work-codex
📰 OpenAI - Apple is getting this wrong
OpenAI addresses Apple’s baseless lawsuit, corrects claims about its employees, and shares messages documenting what happened.
https://openai.com/index/apple-is-getting-this-wrong
📰 OpenAI - How we built a realtime system for responsive voice AI in six months
GPT-Live enables continuous voice interaction with AI, using a turnless speech model and low-latency architecture for faster, more natural conversations.
https://openai.com/index/continuous-voice-interaction-with-gpt-live
OpenAI
New ways to learn and teach with ChatGPT Work and Codex
Explore new education plugins for ChatGPT Work and Codex that help K–12 teachers, college educators, and students learn, teach, research, and build.
📰 NVIDIA - Beyond VLAs: How World Action Models Reshape Robot Manipulation
A central challenge in robotics is building policies that generalize beyond the demonstrations they’re trained on. A policy that succeeds in a training scene…
https://developer.nvidia.com/blog/beyond-vlas-how-world-action-models-reshape-robot-manipulation/
A central challenge in robotics is building policies that generalize beyond the demonstrations they’re trained on. A policy that succeeds in a training scene…
https://developer.nvidia.com/blog/beyond-vlas-how-world-action-models-reshape-robot-manipulation/
NVIDIA Technical Blog
Beyond VLAs: How World Action Models Reshape Robot Manipulation
A central challenge in robotics is building policies that generalize beyond the demonstrations they’re trained on. A policy that succeeds in a training scene often fails when object shapes, positions…