2026-10-19

Tech news with the retellings removed: first-party announcements, measured numbers, and things you can use today.

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186 s · 692,617 input tokens · ≈ $0.0291 · jev-1.13.0 via typesafe

Must-read ≥ 90%

98%releasereddit-ai

Agens Volundr 32B v1: we fixed what you flagged in the Preview. 0/50 loops, session continuation, and an MIT engine that runs it with 262K context (measured to 239K) within a 16 GB budget

/u/ComfortableKindly507

Measured result 98%First-party 97%Usable today 93%

Abstract

https://reddit.com/link/1x8yc8d/video/hosf7c1n27wh1/player Hi r/LocalLLaMA . I'm on the team at Blockway (disclosure: this is our model). Two weeks ago we posted the Volundr 32B Preview here, and you were blunt about what was wrong with it. This is v1, and most of the work went into what you flagged. WHAT v1 FIXES FROM THE PREVIEW "It loops." Repetition loops on SWE-bench tasks: 36/50 → 0/50. "It thinks forever." Reasoning steps used to get cut at 32K. Now the median step is 120 tokens and the longest is 12K. "No prefix caching, it re-reads the whole history every turn." SGLang build: v1 adds session continuation, so follow-up turns reuse the model's state instead of re-prefilling. Follow-up turns are 2.9× faster on our agent replay (1.6 s median with the drafter), with greedy-equivalent outputs (not byte-identical: a cached prefix is prefilled in different pieces). GGUF: uses llama-server's stock --ctx-checkpoints prompt cache, with no custom server code. Our fork only adds the model architecture. Agent results: SWE-bench Verified (50-task subset) 22 → 27/50, τ²-bench 89 → 92/120, median SWE task time 124 → 82 min. "MAKE IT RUN ON CONSUMER GPUS" → AGENS ENGINE (MIT) We're open-sourcing Agens Engine under the MIT licence. It's an inference engine built for this hybrid architecture: 54 of 72 layers keep a fixed-size state instead of a KV cache, and the engine is designed around that. Same GPU, same v1 INT4 weights and drafter, single stream: Agens Engine decodes 1.4–1.6× faster than our own SGLang build (up to 1.8× on code with thinking). Chat 58 → 86, code 79 → 113, agent at 8K context 91 → 121, JSON 125 → 199 tok/s. This is one run per side and compares against our SGLang build only, not stock SGLang, vLLM or llama.cpp, and it is single-stream only. New build: Agens-Volundr-32B-v1-Engine-16GB. It's about 2.85 bits/weight (mixed 2/3/4-bit), gives 262K context (we measured up to 239K) within a 16 GB memory budget, and runs only on Agens Engine. A 12 GB build is on the roadmap. Honest caveats: Speed within the 16 GB budget, single conversation, v1 drafter on: short context: 90 (chat) / 118 (code) / 107 (thinking) / 112 (tool) / 139 (agent, 8K) tok/s after a 219K-token document: 62 / 86 / 77 / 69 / 75 tok/s; 151 s to first token for the 219K prompt a conversation ran to 239K tokens inside the budget weights download is 25 GB These were measured on a 48 GB card under the engine's hard 16 GiB cap (peak 15.5 GiB), not on a real 16 GB card. On a GPU that also drives a display, use a shorter context. Real cards, especially laptop GPUs, may be slower. Post your numbers and we'll add them. At this bit-width, quality drops. GPQA is 73.7 vs 80.3 at bf16 (best of 4 runs each). Coding holds up: HumanEval is 73.2% (1 run) vs 73.8% for INT4. Top-1 agreement with bf16 is 90.4%, KL 0.117. This build is for running locally, not for leaderboard scores. Needs Linux, an NVIDIA driver with CUDA 12.9, the NVIDIA container toolkit, and 32 GB+ host RAM for 262K.docker run --gpus device=0 --ipc=host -p 30000:30000 -v /path/to/Agens-Volundr-32B-v1-Engine-16GB:/model:ro -v /path/to/Agens-Volundr-32B-v1-DFlash2:/drafter:ro ghcr.io/blockwayz/agens-engine:1.0.0 --profile 16gb --model /model --drafter /drafter STRENGTHS (same harness and settings as our comparison model) Competitive coding: LiveCodeBench v6 67.9 (best of 2 runs), +8.4 over Qwen3.8-27B Competition maths: AIME 2025 80.0 (best of 2 runs), +6.7 over Qwen3.8-27B Long context stays cheap: only 18 of 72 layers keep a KV cache. INT4 (24.6 GB) runs on one 48 GB GPU with a single 131K-token input; 262K configured window (validated to 107K). Decode speed stays flat from 1K to 128K. DFlash2 drafter, ships today: JSON / structured output up to 3.0× (125 tok/s INT4 on one 48 GB GPU; 76 tok/s bf16 on a pair) Code 1.9–2.2× Thinking 1.3–1.5× FULL TABLE (Preview → v1; dips included. Where we ran a benchmark more than once, we report the best of N runs. Every run is listed in the model card footnote.) Benchmark Preview v1 SWE-bench Verified (50) 22/50 27/50 τ²-bench 89/120 92/120 LiveCodeBench v6 63.4 67.9 AIME 2025 76.7 80.0 MMLU-Pro 79.9 80.0 MATH-500 98.2 96.8 HumanEval 81.7 78.7 GPQA Diamond 84.3 80.3 CMMLU 86.5 85.9 IFEval 87.6 87.2 IFBench 66.3 64.3 NIAH (to 107K) 100% 100% A few scores went down (GPQA, HumanEval, IFBench). The HumanEval and IFBench dips are within one standard error of the runs; GPQA's isn't. That's the trade-off from tuning for agent behaviour and shorter reasoning. Harness settings are in the footnote under the results table: huggingface.co/Blockway/Agens-Volundr-32B-v1#results-same-harness-for-every-row Thanks to mmssix on Hugging Face for the independent benchmark write-up in our HF discussions. A lot of the above started there. KNOWN LIMITATIONS 32B, not a frontier model, and we don't claim it is. SGLang and llama.cpp still need our builds. Next we're working to upstream the architecture into llama.cpp mainline, and we'll post here when the first PR is open. Stock vLLM can't load it. No Metal/Mac in the llama.cpp path yet. Agens Engine 1.0 is text only. LINKS Collection (all five repos): https://huggingface.co/collections/Blockway/agens-volundr-32b-v1-6ad4817aa6e143550bec1122 Weights: huggingface.co/Blockway/Agens-Volundr-32B-v1 (-INT4, -GGUF, -DFlash2, -Engine-16GB) Agens Engine (MIT): github.com/BlockWayz/agens-engine · ghcr.io/blockwayz/agens-engine:1.0.0 SGLang: ghcr.io/blockwayz/agens-sglang:v1-sm89 (48 GB-class GPUs) / v1-sm90 (141 GB-class GPUs) llama.cpp fork: github.com/BlockWayz/llama.cpp (branch volundr) We'll be in the comments for the next couple of days. "Why doesn't X work yet" questions are welcome. https://preview.redd.it/4d8or6olv6wh1.png?width=2360&format=png&auto=webp&s=a83cbce77656ec8d9f28d9a3f9a5bfb8b96ce989 https://preview.redd.it/koyrx6olv6wh1.png?width=2360&format=png&auto=webp&s=6817372b70f3d4c7230fed90593c0a7015cbcc18 submitted by /u/ComfortableKindly507 to r/LocalLLaMA [link] [comments]

rss:3275145c422e0918Useful?👍👎

97%measurementreddit-ai

Don't sleep on Nemotron 3.5 Super VL

/u/Kahvana

Measured result 97%Usable today 84%

Abstract

Hey everyone, One of the benchmarks I run for myself is to check obscure knowledge, like game rules from Anno 1602 (a game from 1998) and it's (many confusing) editions. Websearch is not permitted. So far, it has come the closest for me in niche information. Ranking order (1 highest, 6 lowest): Nemotron 3.5 Super VL 120B-A12B: Hallucionated salt for citizen to merchant, left out church for settlers, left out required landmarks for merchant and aristocrat Gemma 4 31B IT QAT: Omitted too many parts of the upgrade requirements DeepSeek v4 Flash Vision Exp: Hallucionated different upgrade requirements Qwen3.5-122B-A12B: Hallucionated game editions and upgrade requirements Qwen3.8-flash-next: Hallucionated game editions and upgrade requirements GPT-OSS 120B: Hallucionated game editions and upgrade requirements Compared to the other models, that's a huge improvement! Basically from totally inaccurate to only minor mistakes. The model itself (unsloth's quants, NVFP4) runs at ~8-10 t/s generation and ~150-170 t/s processing on dual RTX 5060 Ti 16GB with dual 48GB DDR5-6000MHz, no MTP, layer split, vulkan. Seems like it's a good model to keep. Also crazy how capable Germma 4 31B IT QAT is. [EDIT] inference engine and models used: llama.cpp 0.7.0, used with models: https://github.com/ggml-org/llama.cpp/releases?page=4#release-b11669 MXFP4 for GPT-OSS-120B: https://huggingface.co/bartowski/openai_gpt-oss-20b-GGUF Q4_K_S for Qwen3.5-122B-A10B: https://huggingface.co/unsloth/Qwen3.5-122B-A10B-MTP-GGUF Q4_K_XL for Qwen3.8-flash-next: https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF Q2_K_XL for DeepSeek v4 Flash Vision Exp: https://huggingface.co/unsloth/DeepSeek-V4-Flash-Vision-Exp-GGUF QAT + MTP (Q4_0) for Gemma 4 31B IT QAT: https://huggingface.co/unsloth/gemma-4-31B-it-qat-GGUF NVFP4 for Nemotron 3.5 Super VL 120B-A12B: https://huggingface.co/unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-GGUF submitted by /u/Kahvana to r/LocalLLaMA [link] [comments]

rss:ae4e321e7113317aUseful?👍👎

96%announcementreddit-industry

I Am Shutting Down adultdatalink.com (Adult Content API) After 2 Years & Open Sourcing The Code on GIthub

/u/adultdatalink

Measured result 96%First-party 92%Usable today 87%

Abstract

Good Afternoon Everyone, Some of you might remember adultdatalink.com from the pornstar stock market index post I made here a while back. That one blew up way more than I expected and the project kept growing from there into a full API for the adult industry. It had scrapers that turned 20+ sites into clean JSON, performer and keyword analytics, scraping projects, and some video tools. After about two years I'm shutting the hosted service down. Instead of letting the code rot on a hard drive, I'm making it open source under MIT: https://github.com/adultdatalink/adultdatalink-public First, I owe some people an apology. The API went down at the end of April and I never said anything. Some of you noticed, and there's even a thread on webdev asking whether it was dead. I stepped away from it without a proper shutdown notice, and anyone who had built something on top of it deserved better. This post is the notice I should have written back then. Since the code is now public, anyone who depended on it can run it themselves. That thread also said the user numbers I posted before were probably fake, and that most people just signed up and connected without really using it. That's not correct. 1,635 people signed up, and 1,008 of them generated API keys. Signups started slow, with 17 in March 2025, peaked at 193 in December 2025, and stayed somewhere between 100 and 190 a month after that. Our own request logs captured about 2.45 million API calls. Almost 2 million of those came between December and April alone, from 303 different API keys, so roughly 430K requests a month. The busiest single day was 25,935 requests. 97.8% of requests came back successful, with a median response time of about a second, which I'm pretty happy with considering every call was scraping a live third party site. On the Cloudflare side, the peak was 3.9 million requests and 36 GB in one 30 day window back in March 2025, from almost 100 countries. A month and a half before that, the same window showed 195K requests. When I added the video tools, bandwidth went up to 407 GB in a single month, and Brazil, Germany, Canada and Spain suddenly became some of the biggest users. Server errors stayed around 0.05% the whole time. Pornhub was by far the most requested source, followed by Eporner, XVideos, xHamster and XNXX. So why shut it down? It just didn't make much money & sunk cost fallacy. I spent so much time building this that I didn't want to just walk away from it fully, but there comes a where you cut losses on bad investment. The usage was real but very concentrated. In the latest logs, one integration made about 80% of all requests, and the top 10 keys made 94%. A lot of people tried it, and a handful actually built on it. In this niche, payment processors, ad networks and app stores all make things harder than they need to be, and the scrapers need constant fixing every time a site changes something. Between hosting and my time, there wasn't much left over. I could keep it going as a small side thing, but I'd rather put the code out there and let people build stuff with it than keep it to myself. The other thing I want to talk about is how much AI has changed this. I built and ran the whole thing basically by myself. That was twelve FastAPI services covering the scraping API, an analytics engine, accounts, payments, logging, a CDN, storage and an ops dashboard, plus the scrapers, a YOLO annotation pipeline, and the stock style indexes. A few years ago that would have taken a small team. AI coding tools have made the barrier to entry for SWE alot lower. Building it is not really the hard part anymore, finding people who will pay for it, getting it in front of them, and keeping it maintained is the hard part, and that's where this one came up short. And also its really not hard for someone to just copy your shit, so software moats are very difficult to maintain IMO nowadays unless paired with hardware. THE REPO The repo has 2 pieces. The first is the adult content API & analytics engine. Everything is configured with environment variable and it runs with pip and uvicorn. It's archived and I won't be maintaining it, so fork it and make it your own. Some scrapers will break as the sites change, that's just how it goes. Please read the README part about terms of service and not exposing the proxy routes to the public internet. And obviously it's 18+ only. Huge thank you to everyone who signed up, built something on the API, sent feedback, reported bugs. I learned a ton doing this and had a lot of fun with it. submitted by /u/adultdatalink to r/SaaS [link] [comments]

rss:04f72eaf80995943Useful?👍👎

96%releasereleases-vllm

vllm v0.32.0

khluu

Measured result 96%First-party 94%Usable today 90%

Abstract

v0.32.0 Highlights This release features 814 commits from 363 contributors (121 new)! Structured decisions and generic parsers : the new POST /v1/systemone endpoint answers choice , noul and score questions over a structured state ( #59299 ), is served by default ( #60677 , #60696 ), and also runs on Cloudflare/clef-omni ( #61064 ), LiquidAI/d1-3B ( #60676 ) and DiffusionGemma ( #60466 ). The new hf tool and reasoning parser ( --tool-call-parser hf , --reasoning-parser hf ) reads the checkpoint's response_template , so models that ship it need no model-specific parser ( #58604 , #58602 ), in both the Python and Rust frontends ( #59005 ). Low-precision KV cache everywhere : NVFP4 KV cache now runs on SM8x and SM12x ( #46963 ), SM90 ( #60620 ) and with XQA decode on SM12x ( #60452 ), and on MiniMax-M3 sparse attention on SM100 ( #59300 ); GLM-5.x defaults to an FP8 KV cache on SM100, halving KV memory per rank with 2.3-5.5% more throughput at a 7-11% TTFT cost in the PR's benchmark ( #60140 , #60281 ); fp8_ds_mla on SM90 gives GLM-5.3-Flash up to 31% more output throughput ( #59246 ); ROCm gains the 4-bit ultraquant_4bit KV cache with about 2x the density of FP8 ( #57057 ). Model Runner V2 : a GPU n-gram speculator with variable-length drafts ( #40704 ), Elastic EP ( #53934 ), pipeline parallelism with prefill context parallelism for MLA models ( #59139 ), a stock torch.compile mode ( #59079 ), and MRV2 is now the default on ROCm with the model-specific MRV1 exceptions removed ( #56360 ). Faster startup and serving : vllm download-kernels installs FlashInfer's precompiled kernels ( #58765 ); vllm preload --preload-autotune keeps the FlashInfer autotune table warm across restarts ( #60085 ); a shared-memory tensor arena broadcasts large multimodal tensors to workers with one copy ( #51207 ); tokenizers and processors load from the local snapshot ( #60825 ); and with --api-server-count above 1 each connection goes to the least-loaded API worker, for 43-85% more req/s on short requests (Qwen3-0.6B, 32 input / 1 output tokens) in the PR's benchmark ( #60723 ). Model support : new EmbeddingGemma 2 omni-modal embeddings ( #60254 ), MiniCPM-V 4.7 ( #58674 ) and Nemotron 3.5 ASR ( #59827 ); video input in the Transformers modeling backend ( #57441 ); GLM-5.3 sparse MLA index conversions reused across layers (3.5-3.9x on that kernel, #59464 ) and fused multi-step draft decode (13.3% at concurrency 1, #57443 ); HiSparse per-request residency caching for up to 58% more throughput on GLM-5.3-NVFP4 on B200 with long, fully GPU-resident contexts at high concurrency ( #60083 ). Security : fixes for GHSA-4hhp-h66f-j5j7 (chat template resource exhaustion, #50300 ), GHSA-q97c-p368-75c4 (tokenizer max_token_id , #59491 ) and GHSA-frvg-qw88-hww9 (P2P ZMQ socket exhaustion, #51504 ); CORS no longer allows every origin by default ( #61009 ); untrusted images are limited to raster formats ( #60022 ); shared-memory multimodal cache handles are signed ( #59357 ); and JSON schemas nested deeper than 64 levels are rejected ( #60036 ). Breaking changes : Python 3.10 dropped ( #60402 ); default CORS origins restricted ( #61009 ); transformers>=5.16.1,<5.20.0 and tokenizers>=0.23.2 ( #57387 , #59796 ); torch.compile sequence parallelism and AsyncTP passes removed ( #60517 ); cascade attention removed ( #61126 ); mamba_cache_mode="all" removed ( #58997 ); max_tokens no longer reserved from the context window ( #57035 ); Responses API reasoning stream events renamed ( #59652 ). Release Artifacts Python Wheels Platform Install PyPI (CUDA 13.0) pip install vllm PyPI (CUDA 13.0, uv) uv pip install vllm --torch-backend=auto ROCm pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/0.32.0/rocm723 XPU uv pip install vllm --extra-index-url https://wheels.vllm.ai/0.32.0/xpu --extra-index-url https://download.pytorch.org/whl/xpu --index-strategy unsafe-best-match Docker Images Platform Docker Image CUDA 13.0 (Default) docker pull vllm/vllm-openai:v0.32.0 CUDA 12.9 docker pull vllm/vllm-openai:v0.32.0-cu129 CUDA 13.0 + Ubuntu 24.04 docker pull vllm/vllm-openai:v0.32.0-ubuntu2404 CUDA 12.9 + Ubuntu 24.04 docker pull vllm/vllm-openai:v0.32.0-cu129-ubuntu2404 ROCm docker pull vllm/vllm-openai-rocm:v0.32.0 CPU docker pull vllm/vllm-openai-cpu:v0.32.0 XPU docker pull vllm/vllm-openai-xpu:v0.32.0 Other Artifacts Pre-built release artifacts are available in the Assets section at the bottom of this page, including: Source distribution tarball CUDA 12.9 Python wheels for x86_64 and arm64 CUDA 13.0 Python wheels for x86_64 and arm64 CPU Python wheels for x86_64, arm64, and macOS XPU Python wheel for x86_64 Model Support New models : EmbeddingGemma 2 ( google/embeddinggemma-2 ), an omni-modal text/image/audio/video embedding model, with unused modality encoders skipped through --limit-mm-per-prompt ( #60254 , #60289 ); MiniCPM-V 4.7 ( #58674 ); Nemotron 3.5 ASR ( nvidia/nemotron-3.5-asr-streaming-0.6b ) complete-audio transcription; native realtime streaming is not yet supported ( #59827 ); Cloudflare/clef-omni ( #61064 ) and LiquidAI/d1-3B ( #60676 ) on /v1/systemone ; PLaMo 3 reasoning and tool parsers ( #56575 ); and the zerank-2 reranker score template ( #60415 ). Transformers modeling backend : video inputs ( #57441 ), and Glm, Arcee, CWM, Mellum ( #59679 ), GPT-NeoX, Phi, Seed-OSS and Jais2 ( #59701 ) now served through it. New model capabilities : LoRA for BERT, RoBERTa and XLM-RoBERTa embedding models ( #61033 ); Granite as an EAGLE3/DFlash target ( #60855 ); Qwen3-ASR EAGLE3 ( #52824 ); Sarvam MLA EAGLE3/DSpark with PP ( #55902 ); GLM-5.3-Flash with the DFlash2 drafter ( #56983 ); and MiniMax-M3 EAGLE3 at PP > 1 on MRV2 ( #57197 ) with MTP sharing target embeddings under PP ( #58648 ). GLM-5.3 and GLM-5.3-Flash : sparse MLA index conversion reused across layers ( #59464 ), fused multi-step draft decode ( #57443 ), FP8 KV cache default on SM100 ( #60140 , #60281 ), fp8_ds_mla on SM90 ( #59246 ), qlnorm skipped for MHA (4.4-7.7% TTFT, #58845 ), long-context indexer prefill sharded across TP (up to 25% lower TTFT at 1M context, #54951 ), DCP for the kpool indexer ( #59211 , #60032 ), KDA prefill checkpoints with FlashKDA ( #56960 ), FP8 KV on SM90 sparse MLA ( #55222 ), --mamba-ssm-cache-dtype honored for KDA state ( #59147 ), a generic packed KV layout ( #57169 ), MTP index sharing restored ( #61079 ), and fixes for vision-tower crashes ( #59126 ), non-square image encoder budgets ( #59565 ), DP startup ( #56974 ), video timestamps ( #55647 ), DFlash2/EAGLE3 aux hidden states ( #60763 ) and null-block writes ( #59528 ). DeepSeek-V4 and V4.1 : decoder-side SWA bounded replay ( #58132 ) with replay layers in CUDA graphs and trimmed in PIECEWISE graphs ( compilation_config.decoder_replay_trim_threshold , #59532 ) plus last KV-source layer trimming (+3.6%, #59894 ), packed FP8 Engram exchange across DP ( #59923 ), faster CPU-offloaded Engram lookups ( #59327 ), fused DeepSeek V4 + MTP drafting ( #58463 ), encoder CUDA graph packing for V4.1 and Kimi-K3 (11% lower mean TTFT, #58925 ), EPLB for DSpark drafts ( #57549 ), and fixes for NVFP4 checkpoints with 0% draft acceptance ( #59655 ), MegaMoE shared-expert finalize ordering ( #59927 ), worst-case vision dummy inputs ( #57898 , #59271 ), compressor ring null-block writes ( #58560 ), Triton 3.9 indexer quantization ( #60716 ) and decoder-replay CUDA graphs with FlashInfer EP MoE backends ( #61041 ). Qwen : Qwen3.8-Flash-Next (Qwen4Exp) merged QSA projections in one GEMM (1.45-1.68x, #59533 ), fused QSA pre-indexer ( #57097 ), sequence parallelism through --enable-hc-sp ( #56322 ), SM100 low-latency decode GEMM plans (up to 2.7x vs cuBLAS on small-M, #59214 ), --kv-cache-dtype-skip-layers honored ( #60023 ), and PLE loading for INC ( #59990 ), Quark ( #59443 ) and pre-SM89 GPUs ( #60142 ); Qwen3-VL/Qwen3.5/Qwen3.8 M-RoPE cache 4x smaller and Qwen3.5 YaRN matching Transformers ( #59853 ); Qwen3-Omni M-RoPE fixes ( #58890 , #59842 ); Qwen3-ASR on the upstream processor ( #57146 ); Qwen3-VL unknown video fps ( #60501 ) and per-video options with the multimodal cache ( #55203 ). Kimi-K3 and MiniMax-M3 : Kimi-K3 FlashInfer fused speculative KDA on SM100 ( #54255 ), fused vision processor (10-20x preprocessing at 1080p-8K, #59278 ), fused K/V pack on the DCP prefill path ( #56881 ), and DSpark KV cache back to 4 groups ( #59733 ); MiniMax-M3 MSA prefill with an E4M3 query (26-37% faster with FP8 KV, #60684 ), FP8 indexer cache on non-SM100 GPUs ( #59081 ), Triton indexer tuning ( #56151 , #59481 ), and indexer score fixes ( #60194 , #54597 ). Multimodal : device-side input normalization for Qwen3-VL/Qwen3.5/Qwen4Exp ( #51289 ), GLM-V and GLM-OCR ( #55389 ), Llama Nemotron VL ( #57928 ) and Kimi K2.5/K3 ( #59278 ), kept fused under compile_mm_encoder ( #59195 ); encoder inputs sorted by modality (6.5-8.2% more req/s, #60196 ); Mistral 3 preprocessing 8% faster ( #57531 ); Pixtral compile_mm_encoder ( #57168 ); and fixes for Llama 4 / Mistral Large 3 EAGLE drafters (regression since v0.28.0, #57633 ), Phi-4 MM audio OOM ( #60614 ), DeepSeek-OCR zero-sum pixels ( #59417 ), MiMo-V2.5/V2.6 with fastsafetensors ( #58142 ), MiMo-V2.6 FP8 KV cache ( #58128 ), Voxtral realtime ( #45022 ), Nemotron Super vision LayerNorm ( #61401 ), K-EXAONE config and MTP RoPE ( #60187 ), Nemotron Parse tied lm_head ( #60272 ), LongCat-Flash MTP loading ( #60080 ) and Ling-3.0-flash-VL ( #59026 ). Engine Core Model Runner V2 : GPU n-gram speculator ( #40704 ), Elastic EP with drain-then-commit scaling ( #53934 ), PP with PCP for MLA models ( #59139 ), STOCK_TORCH_COMPILE mode ( #59079 ), dynamic K in autoregressive speculators ( #57053 ), MTP sampling-mask replay ( #59359 ) and per-module LM heads ( #58921 ), non-blocking H2D copies on WSL2 ( #60215 ), and fixes for int64 index offsets at long context ( #60736 ), warmup ( #61013 ), GDN prefill checkpoints ( #59536 ) and MRV2+PP+MTP align state ( #60210 ). Speculative decoding : fused multi-step draft decode on FlashInfer trtllm-gen ( #58371 ), FlashInfer ReplaySSM for Mamba2 MTP ( #52928 ), in-graph metadata for DFlash/DSpark drafts ( #60692 ), and correctness fixes for ngram on hybrid recurrent models ( #56531 ), Mamba prefill checkpoints with MTP and prefix caching ( #59759 , #60797 ), stop-string trimming ( #47616 ), draft model config ( #61240 ), adaptive block verification ( #60945 ), DSpark bonus slots ( #59105 ) and draft models on the Transformers backend ( #59293 ). Hybrid models and prefix caching : Mamba2 internal prefill checkpoints for NemotronH, Granite-4.0-H, Falcon-H1 and Zamba2 ( #57329 ), stale block hashes dropped when streaming sessions truncate ( #59103 ), and fixes for quantized Mamba-2 with TP ( #58083 ), spec decode page sizes ( #59975 ), prefix-cache bounds ( #55601 , #60533 ) and sm90 Mamba2 NaNs ( #61718 ). Sleep and RL : sleep releases workspace scratch (up to 4.5 GiB/GPU, #59156 ) and KV-init state (about 4.6 GiB/GPU, #59158 ), opt-in sleep_mode_offload_cudagraph ( #59160 , #59523 ); token-level weight-version spans on outputs ( #59582 , #61070 ); weight-transfer communicators released without stopping inference ( #61060 ); --sleep-preserve-parameter-names 'draft.*' ( #61046 ); modelexpress weight-transfer backend ( #58399 ); POST /weight_checker ( #51350 ); weight-transfer metrics ( #55781 ); --enable-nccl-comm-suspend releasing the MNNVL workspace ( #59360 ); and sleep(level=2) keeping FP8 KV scales ( #57163 ). HiSparse : per-request residency cache (up to 58% at high concurrency with GPU-resident contexts, #60083 ), lower scheduler overhead ( #57930 ), GPU residency owned by HiSparse ( #60647 ), less staging memory ( #60429 ), host cache gauges ( #58949 , #59485 ), and fixes for decode token limits ( #60682 ) and TP hangs ( #60941 ). Startup and memory : shared-memory tensor arena ( --enable-shm-tensor-arena , on by default, #51207 ), Fast Start with PP ( #55477 ), daemon-held weights counted against gpu_memory_utilization ( #57298 ), autotune preload ( #60085 ), local tokenizer snapshots ( #60825 ), sparse_mla_prefill_workspace_factor saving several GiB at 1M context ( #61023 ), cached memory reclaimed during weight loading ( #57516 ), fewer Triton sampler warmup compiles ( #58605 ), sharded state loader with PP ( #52110 ), and reload_weights with Run:ai streamer ( #58124 ). Kernels and runtime : MoE weighted-sum tuning (1.17-2.37x, #59731 ), flash-maxsim kernels for ColBERT/ColPali scoring ( #40337 ), fallback detokenization with 69% less CPU at 32K tokens ( #60497 ), faster KV block free queue ( #60269 ), Triton kernels off tl.make_block_ptr ( #60502 ), fusion passes restored under PyTorch 2.15 ( #57299 ) and static FP8 quant fusion on recent torch ( #61108 ). Batch invariance : custom all-reduce under VLLM_BATCH_INVARIANT=1 ( #58623 ), deterministic split-K LoRA shrink ( #59377 ), every attention backend checked ( #60271 ), SM120 TP configs ( #58495 ), and ROCm support ( #52231 ). Observability : vllm:prompt_tokens_cached_by_source_total ( #56318 ), vllm:num_requests_kv_fetch_by_stage ( #58874 ), vllm:admission_rejections_total ( #55812 ), vllm:watermark_requests ( #58715 ), --custom-histogram-buckets ( #48867 ), a JSON log formatter via LoggingConfig.formatter ( #58739 ), and per-session profiling controls ( #57875 ). Large Scale Serving MoE communication : FlashInfer CuteDSL MegaMoE for more models via --moe-backend flashinfer_cutedsl ( #54049 ), FP8 combine for the FlashInfer one-sided all2all (+4% at DEP8, #57995 ), NCCL symmetric-memory MoE reduce-scatter (9.6-16% lower latency, #49194 ), DeepEPv2 hybrid NVLink+RDMA mode on by default ( #57991 ), Kimi-K3 shared experts with DeepEP v2 ( #56875 ), and opt-in EPLB migration batching ( #52641 ). Context parallelism : decode requests sharded across PCP ranks ( #52162 ) and TokenSpeed MLA with block-interleaved DCP for heterogeneous NIXL disaggregation ( #59462 ). KV connectors : NIXL host-buffer copies coalesced ( #54483 ), mixed block sizes ( #58860 ), peer metadata recovery ( #55471 ) and Qwen3.8-Flash-Next MTP acceptance in P/D ( #59997 ); Mooncake coalesced hybrid/MLA RDMA regions ( #57952 ) and bootstrap retries ( #55923 ); MoRIIO Kimi-K3 DSpark READ ( #57700 ); DeepSeek-V4.1 P/D skipping bounded replay for loaded KV ( #59197 ); and KVCR pool-and-index API ( #59899 ). Data parallelism : streaming requests pinned to their engine ( #59017 ), per-engine RNG streams ( #59788 ), and placement groups without ray[default] ( #57648 ). Hardware & Performance NVIDIA : native 64-head FlashMLA sparse prefill on SM100 (1.16-1.18x TTFT, #60029 ), block-FP8 DeepGEMM experts skipping padding ( #59128 ), SM120 split-K attention ( #58482 ), DGX Spark Qwen4Exp GEMM plans ( #59753 , #59632 , #60027 ), OAI Triton MXFP4 MoE on SM12x ( #58877 ), and FlashInfer autotune cache per rank ( #57635 ). AMD ROCm : DeepSeek-V4 AITER MegaMoEV2 via --moe-backend aiter_mega_moe ( #59685 ) with opt-in shared-expert fusion (3-8%, #61004 ), opt-in mono decode for DeepSeek-V4.1-Flash on MI355X ( VLLM_ROCM_MONO_DECODE=1 , #60397 ), opt-in Gluon sparse MLA (up to +16% on GLM-5.2, #53492 ), paged MXFP4 indexer ( #58671 ), opt-in a4w4 MoE for DSv4 ( #58819 ), ultraquant_4bit KV cache ( #57057 ), aiter_flydsl GDN prefill ( #57560 ), Kimi-K3 prefill checkpoints (about 20% better TTFT, #58344 ), fused MLA decode ( #57640 ) and per-rank latent MoE shards ( #59591 ), GLM-5.3-Flash split-K sparse MLA decode (5-15% lower ITL, #58584 ), native merge_attn_states (2.4x, #60159 ), ROCM_SEGMENTED_ATTN for RDNA ( #59132 ), gfx11 W4A16 tuning (up to 23% TTFT, #56301 ), MRV2 default with model-specific MRV1 exceptions removed ( #56360 ), and fixes for sleep-mode segfaults on ROCm 7.2+ ( #60367 ) and MLA weight reloads ( #60389 ). Intel XPU : MLA in FLASH_ATTN_MLA ( #58297 ), Arc Pro B70 FP8 GEMM (2.0-2.73x, #56063 ) and fused MoE configs ( #53065 ), fused SYCL LayerNorm ( #57172 ), and fixes for Gemma-2 softcap ( #58936 ) and DP+EP MoE ( #57787 ). CPU : vectorized sampler (up to 58x on s390x, #53913 ), aarch64 causal conv1d (about 9%, #54093 ), POWER10 AWQ/GPTQ ( #59149 ), and PowerPC dtype=auto choosing bfloat16 ( #58528 ). Other : tpu-inference v0.31.0 ( #60190 ) and out-of-tree platforms registering multiple attention backends ( #41091 ). Quantization New capabilities : online re-quantization of linear layers ( --quantization-config.linear fp8_per_channel , #55684 ), per-token NVFP4 CuTe-DSL MoE ( #50030 ) with ReLU2 ( #56740 ), MXFP4 MoE oracle with CUTLASS W4A4 and BF16 fallback ( #60079 ), compressed-tensors MXFP4 MoE with FP8 activations ( #59815 ), ModelOpt IQ2_XS/IQ2_XXS/Q8_0 on SM120/SM121 ( #59604 ), int4_per_token_head KV with non-power-of-two heads ( #56198 ), and Humming preferred over Marlin on SM90 ( #56997 ). Fixes : silent CUTLASS NVFP4/MXFP4 MoE corruption under EP ( #59042 ), GPTQ with non-default safetensors names ( #60411 ), moe_wna16 8-bit asymmetric zero points ( #58950 ), and ModelOpt mixed-precision NVFP4 detection ( #56050 ). API & Frontend New endpoints and options : /v1/systemone ( #59299 , #60677 , #60696 ), hf parser ( #58604 ), vllm download-kernels ( #58765 ), output_mode: "text" and stop_reason in /inference/v1/generate ( #58588 , #60251 ), mm_payload for render requests ( #61427 , #60728 ), Responses min_p and stop_token_ids ( #48084 , #56592 ), LLM.chat(priority=...) ( #57243 ), and /generative_scoring cache_salt ( #60841 ). Behavior : max_tokens is an upper bound instead of a reservation, so long prompts are no longer rejected ( #57035 ); least-loaded API worker selection ( #60723 ); tool parser compatibility checked at startup ( #59749 ). Tool calling and structured output : parallel_tool_calls=false enforced ( #50502 , #59298 ), tool_choice="required" streaming ( #60354 ) and native XML parsers ( #47512 ), Step-3.5 and minimax_m3 on the parser engine ( #59321 , #59743 ), xgrammar propertyNames ( #57594 ), and LoRA PEFT rank_pattern / alpha_pattern applied with unsupported features rejected ( #59801 , #60225 ). Rust frontend : --grpc-port ( #59659 ), --stream-interval ( #60589 ), logprob_token_ids ( #59549 ), Control.Shutdown with vllm-proto 0.5.0 ( #59316 ), qwen3_json parser ( #60136 ), and Kimi K3 grammar fixes ( #59190 , #59395 ). Security GHSA-4hhp-h66f-j5j7 : chat templates are validated statically and rendered under a timeout ( VLLM_CHAT_TEMPLATE_RENDER_TIMEOUT ), user-supplied chat_template in Responses chat_template_kwargs is rejected, and the Rust frontend caps template execution ( #50300 ). GHSA-q97c-p368-75c4: tokenizer max_token_id off-by-one fixed ( #59491 ). GHSA-frvg-qw88-hww9: P2P ZMQ peer sessions are bounded by VLLM_P2P_MAX_PEERS (default 128), VLLM_P2P_HANDSHAKE_TIMEOUT_S (default 30) and VLLM_P2P_IDLE_TIMEOUT_S (default 300), so unique peer addresses can no longer exhaust ZMQ sockets ( #51504 ). Default CORS settings no longer allow every origin in the Python and Rust frontends; set --allowed-origins to permit browser origins ( #61009 ). Untrusted images are opened with a raster-format allowlist ( ALLOWED_IMAGE_FORMATS ), denying EPS/PostScript and WMF ( #60022 ); shared-memory multimodal cache handles are HMAC-signed and forged handles rejected ( #59357 ). Structured-output JSON schemas nested more than 64 levels return 400 ( #60036 ); chunked /v1/embeddings requests keep cache_salt ( #47696 ); Phi-4 MM audio no longer holds multi-GiB attention matrices ( #60614 ); DeepSeek-OCR zero-sum pixel tensors no longer kill EngineCore ( #59417 ). Security dependency bumps: aiohttp>=3.14.3 , pillow>=12.3.0 , cryptography, urllib3, pyjwt and others ( #59249 , #59315 , #59427 ). Dependencies transformers>=5.16.1,<5.20.0 (minimum raised from 5.10.4, #57387 , #59762 ) and tokenizers>=0.23.2 ( #59796 ). NIXL 1.5.0 ( #56907 ), CuTe DSL 4.8.0 block-scale APIs ( #59480 ), humming-kernels==0.1.17 with initial Rubin support ( #61556 , #61858 ), InstantTensor 0.2.2 ( #61167 ), and flashinfer-jit-cache excluded from wheel install_requires ( #60052 ). ROCm: AITER 0.1.24.post1 ( #59794 ) and MoRI v1.2.3.post1 ( #56073 ). XPU: auto-round-lib 0.16.0 ( #60976 ). TPU: tpu-inference v0.31.0 ( #60190 ). Build Python 3.11-3.14 supported ( #60402 ); macOS wheels built with the stable ABI ( #60312 ); CUDA source builds require GCC 13 and fail early otherwise ( #60259 ); Docker images compile Python bytecode at build time for faster container startup ( #55422 ); VLLM_USE_PRECOMPILED finds a compatible published wheel within the last 20 commits ( #58871 ); the vendored DeepGEMM uses TORCH_LIBRARY (abi3, #48962 ). Breaking Changes & Deprecations Python 3.10 is no longer supported; the minimum is Python 3.11 ( #60402 ). Default CORS origins changed from * to none; set --allowed-origins for browser clients ( #61009 ). transformers>=5.16.1,<5.20.0 and tokenizers>=0.23.2 are required ( #57387 , #59796 ). The torch.compile sequence parallelism and AsyncTP passes were removed, along with PassConfig.enable_sp , fuse_gemm_comms and sp_min_token_num ; model-level sequence parallelism is unaffected ( #60517 ). Cascade attention was removed; --disable-cascade-attn still parses but only warns and will be removed in v0.33.0 ( #61126 ). mamba_cache_mode="all" was removed; --mamba-cache-mode all now fails validation ( #58997 ). Previously deprecated APIs were removed: wait_for_inflight_requests (use mode="wait" ), AsyncLLM.pause_generation() , IOProcessor.output_to_response , vllm.scripts.main() and vllm bench throughput --dataset ( #60923 ). The sonnet benchmark dataset was removed as scheduled ( #60129 ). Tensorizer support was removed from LoRA ( #60024 ), and the PyO3 vllm._rust_tool_parser bridge was removed ( #59744 ). API changes: max_tokens is treated as an upper bound on output rather than reserved from the context window ( #57035 ); Responses API streaming uses response.content_part.added/done with part.type="reasoning_text" instead of response.reasoning_part.added/done ( #59652 ); SamplingParams.prompt_logprob_token_ids is per row ( #56984 ); /inference/v1/generate logprobs use integer token IDs ( #58181 ); sleep API responses are standardized and invalid levels return 400 ( #52864 ); return_mm_kwargs is deprecated in favor of mm_payload ( #61427 ). Watermarking: when the engine has watermarking configured, an omitted request field means enabled and only false opts out; unsupported combinations warn and fall back ( #56801 ). Rust frontend: qwen3_xml now parses Qwen3-Coder XML (alias of qwen3_coder ); use the new qwen3_json for the JSON format ( #60136 ). New defaults: shared-memory tensor arena on ( #51207 ); GLM-5.x FP8 KV cache on SM100 with --kv-cache-dtype auto ( #60140 , #60281 ); VLLM_DEEPEP_V2_ALLOW_HYBRID_MODE=1 ( #57991 ); Humming preferred over Marlin on SM90 ( #56997 ); sparse_mla_prefill_workspace_factor 2 above 256K max_model_len ( #61023 ); MRV2 on ROCm with model-specific MRV1 exceptions removed ( #56360 ); VLLM_ROCM_AITER_MLA_DCP_VERIFY=auto ( #59965 ); dtype=auto picks bfloat16 on PowerPC ( #58528 ); MXFP4 W4A4 checkpoints fall back to W4A16 with a warning when no W4A4 kernel exists ( #60079 ). Stricter validation: incompatible --prefix-match-unit ( #58021 ), max_num_scheduled_tokens=0 ( #60714 ), HiSparse with cudagraph_mode=FULL ( #59688 ), and negative --device-ids ( #49053 ) are rejected at startup. New Contributors @0xViking made their first contribution in #60053 @1fanwang made their first contribution in #40986 @a-sidorova made their first contribution in #55335 @AbhayShuklaIIT made their first contribution in #59763 @ahmed-bsod made their first contribution in #58655 @ai-yang made their first contribution in #58088 @AIwork4me made their first contribution in #54706 @AlanFokCo made their first contribution in #57163 @AMD-yanfeiwang made their first contribution in #61004 @aniskumar-nv made their first contribution in #53020 @Anurag-M1 made their first contribution in #57693 @Aravind-11 made their first contribution in #60098 @awesome-pro made their first contribution in #58895 @Ayu190505 made their first contribution in #57995 @beenpow made their first contribution in #57648 @big-yellow-duck made their first contribution in #59132 @blueberry808 made their first contribution in #58932 @bluemelov1 made their first contribution in #58560 @bnkaiteam made their first contribution in #60507 @brandonfzhang made their first contribution in #56881 @Btwestyo made their first contribution in #59924 @bvolpato made their first contribution in #55203 @C1ves made their first contribution in #58890 @charlesli640 made their first contribution in #59977 @cheese-cakee made their first contribution in #59079 @CorgiBoyG made their first contribution in #55596 @damienlaine made their first contribution in #45022 @danichan-mkm made their first contribution in #59285 @danigarciaoca made their first contribution in #56372 @debanshd made their first contribution in #57476 @devpramod made their first contribution in #60855 @devshah-cohere made their first contribution in #60286 @DGlan made their first contribution in #59875 @dhairyashilRG made their first contribution in #58911 @DOCCA0 made their first contribution in #52641 @Dovis01 made their first contribution in #56994 @ebfio made their first contribution in #58165 @ejrtks1020 made their first contribution in #59180 @eky-amd made their first contribution in #54857 @gaby made their first contribution in #59126 @haotiaa made their first contribution in #60766 @harshaladhav-amd made their first contribution in #58268 @hdq66666 made their first contribution in #59695 @hoaivoke made their first contribution in #61033 @hpdkhoa made their first contribution in #60747 @hubert-marek made their first contribution in #61000 @hubunt made their first contribution in #47512 @huishi-hs made their first contribution in #58053 @hyukjlee made their first contribution in #59965 @Id545 made their first contribution in #59247 @ieaves made their first contribution in #52824 @ischencheng made their first contribution in #60763 @Jeff-Tseng-dev made their first contribution in #59396 @jiangyunfan1 made their first contribution in #57780 @JimmyWang0417 made their first contribution in #61081 @jin-amd made their first contribution in #58904 @JRJRJPRO made their first contribution in #59879 @Kevinli24 made their first contribution in #58878 @Kylinny made their first contribution in #60354 @Lcy0801 made their first contribution in #58975 @Legend-2727 made their first contribution in #59293 @lianakoleva made their first contribution in #60136 @LiRunGuo made their first contribution in #56198 @lobo235 made their first contribution in #60364 @LostFox11 made their first contribution in #58542 @MadhavJivrajani made their first contribution in #57635 @MatthewCYM made their first contribution in #57146 @Mazukiri made their first contribution in #60036 @MohammadHijjawi97 made their first contribution in #59072 @MohitAMD made their first contribution in #55053 @nishantrevur made their first contribution in #59541 @njriasan made their first contribution in #60502 @NNNtrance made their first contribution in #55647 @NolenLiang made their first contribution in #56974 @notimesea made their first contribution in #59105 @ntheanh201 made their first contribution in #57734 @nv-hwoo made their first contribution in #58399 @ojeda-e made their first contribution in #59107 @Ostring24 made their first contribution in #50502 @peperunas made their first contribution in #58938 @pondzikk made their first contribution in #55601 @praveingk made their first contribution in #60161 @puneetsharma21 made their first contribution in #48074 @rdlh made their first contribution in #60415 @rmhaskarnvidia made their first contribution in #59081 @roG0d made their first contribution in #55937 @roipony made their first contribution in #40337 @shijie-lyu made their first contribution in #55771 @shiyuan680 made their first contribution in #51350 @shreyanshj10 made their first contribution in #60359 @siliangchen-amd made their first contribution in #57108 @Sohaib-Ahmed21 made their first contribution in #59827 @spa5k made their first contribution in #60497 @sricursion made their first contribution in #60307 @SunnyR made their first contribution in #59811 @superdiaodiao made their first contribution in #55717 @surajsharan made their first contribution in #60225 @surawut-jirasaktavee made their first contribution in #60876 @Thiago4532 made their first contribution in #58014 @TommyTranX made their first contribution in #48039 @TylerMa-debugg made their first contribution in #59917 @ultramancode made their first contribution in #60786 @usmanmateen made their first contribution in #59336 @v-shobhit made their first contribution in #59655 @vaclavcadek made their first contribution in #49602 @viktorkou made their first contribution in #58124 @vovani made their first contribution in #60194 @WarrierRajeev made their first contribution in https://github.com/vllm-project/vllm/pull/59135 @www6v made their first contribution in https://github.com/vllm-project/vllm/pull/59749 @xiaofei-zheng made their first contribution in https://github.com/vllm-project/vllm/pull/55104 @xiaoyu-xyz made their first contribution in https://github.com/vllm-project/vllm/pull/55161 @xuantengh made their first contribution in 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Same story elsewhere (1)

vllm v0.32.1rc0 releases-vllm

rss:4352cb0e2cf51aa7Useful?👍👎

95%analysisreddit-ai

72 hours, four big open weights drops. The only one that fits in 24GB came from NVIDIA, of all companies.

/u/BTA_Labs

Measured result 95%Usable today 83%First-party 42%

Abstract

What a week. Quick recap, all numbers from the official repos and model cards: Thursday: Meta drops Muse Spark Open. 816B total, 42B active, 1M context. The launch post says it brings agentic AI "to your desktop". The desktop is two M5 Ultra Mac Studios with 512GB each, a config Apple says ships "late October" and still hasn't put a price on. The alternative is four 256GB Studios, roughly $38k. On top of that it's a custom license: mandatory "Built with Muse Spark Open" attribution and a separate deal once you're past $20M revenue. And Meta's own model card puts it at 39 on the Artificial Analysis index, behind GLM 5.3 (45) and Kimi K3 (44). Same day: NVIDIA ships Nemotron 3.5 Bolt. 74B total, 9B active, trained quantization aware down to 2 bit, so the whole thing is 22GB plus a 1.7GB vision projector. Built to fit in 24GB on purpose. Unsloth GGUFs on day one, plus llama.cpp, Ollama and LM Studio support. Friday: Reflection actually ships Beam. 501B total, 23B active, Apache 2.0, no strings. Smallest official checkpoint is NVFP4 at 294GB. Saturday afternoon: DeepSeek posts V4.1 Flash 1017 on X like it's nothing. MIT. Their own table has it at 77.0 on DeepSWE vs 74.0 for Opus 5 (vendor numbers, salt accordingly). The repo is 510GB. So I put them all on one chart against the 24GB line most of us live under. My takeaways: "Runs locally" now officially means "runs on a computer that costs more than a car." The company that keeps selling us 24GB cards is the only lab this week that built a model for 24GB cards. Not sure if that's wholesome or a sales funnel. Licenses mattered more than parameter counts this week. MIT and Apache 2.0 from DeepSeek and Reflection, attribution plus a revenue clause from the company with the loudest "open" marketing. Mistral says the Large 4 weights ("Le Chonk", 1T total) land before the end of the month, so the 24GB line is about to get even lonelier. Sources in the comments. submitted by /u/BTA_Labs to r/LocalLLaMA [link] [comments]

rss:15dd6bbd60d4ef19Useful?👍👎

94%measurementreddit-ai

Embedding model can be used to search text inside image

/u/jupiterbjy

Measured result 94%Usable today 39%

Abstract

Just something I've been testing over few week since Embedding Gemma 2 released which is about 330MB at Q8. Not sure if this would count as 'resource' flair... Image shows cosine similarity changes as I refine the text query (aka text embedding vector) over time. Shows how well it work & quirks it has at same time. I'll leave tl;dr version for now, 3:30 am here. If anyone is interested I'll do few more tests and make example query image like above - still have 7 more sets totaling 22448 screenshots with mix of eng / kor / jpn already indexed, but I'm out of test ideas. TAKE WITH GRAIN OF SALT because this was tested only on few sets that isn't really scientific. TL;DR for EmbeddingGemma2 at least: For it's size & embedding dim (330MB Q8 / 768 embedding dim) this is very much usable This understands a word's meaning across language a bit but has much lower similarity (again, prob due to size) query cos sim. delta to next candidate image with sentence 삼각건이 머리에 얹어졌다 0.71156 +0.00810 (38.jpg) image with sentence triangular cloth hat was place on top of head 0.62414 +0.00015 (38.jpg) Requires careful wordings (e.g. blond vs blonde) - maybe SLM's limitation or embedding dim's limitation (at least eng & kor) Text seemingly DOMINATE cosine similarity more than visual traits sometimes adding visual trait HARM (refer chair above) the similarity not sure if it's due to text being more informationally dense (aka more 'surprising') or just model being small and not recognizing such visual traits, idk regardless, visual traits are less useful in this test set since like 90% of 3003 imgs would have blonde girl in it. query cos sim. delta to next candidate image with dialog '삼각건이 머리에 얹어졌다' 0.75960 +0.01774 image with blonde girl 0.68208 +0.00250 image with blonde girl making >< expression 0.70416 +0.00340 (at least kor) Word's existence dominate over whole sentence e.g. for above image's example "삼각건"(cleaning hat thing) dominates more than other words around it fyi only 2 image out of 3003 contains world "삼각건", which you can see via delta gap drops query cos delta image with word 삼각건 0.69251 +0.01211 / +0.00766 / +0.00008 image with word 삼각건 & 머리 0.72881 +0.01331 / +0.02728 / +0.00203 image with sentence 삼각건이 머리에 얹어졌다 0.71156 +0.00810 / +0.01660 / +0.00041 Test setup: Visual novel screenshots of a single route 3003 screenshots from hanasaki workspring 3078 scs from irotoridori no hikari EmbeddingGemma2 via llama cpp model = KV = Q8_0 nb = b = ctx = 4096 (embedding model restriction, all need to be same) RX 7900GRE via vulkan (f u rocm, so annoying to build) Sample output for anyone curious about processing time Index mode: $ py embed_img_▽.py "./하나사키워크스프링 히카리" Batch: 8 Resize width: 1280 Resize threshold: 1536 [VectorDB] open w/ 0 rows removing 0 missing records from DB Checking new/modified images in dir Got 3003 new/modified imgs [EmbedServer] open for http://127.0.0.1:8081 100%|██████████| 376/376 [08:30<00:00, 1.36s/batch] [EmbedServer] closed [VectorDB] closed Indexed 3003 new/modified files in 510.10 (5.887/s) Query mode: $ py embed_img_▽.py "./하나사키워크스프링 히카리" -q "image with dialog '삼각건이 머리에 얹어졌다' with blonde girl making >< face" [VectorDB] open w/ 3003 rows [EmbedServer] open for http://127.0.0.1:8081 [EmbedServer] closed [VectorDB] closed Query: "image with dialog '삼각건이 머리에 얹어졌다' with blonde girl making >< face" Top-5: 0.76813 +0.01052 Artemis_2026-09-08-00-57-38.jpg 0.75761 +0.00773 Artemis_2026-09-08-00-57-26.jpg 0.74988 +0.00341 Artemis_2026-09-07-02-36-38.jpg 0.74647 +0.00067 Artemis_2026-09-07-02-36-52.jpg 0.74580 -------- Artemis_2026-09-08-01-58-14.jpg '.../하나사키워크스프링 히카리/Artemis_2026-09-08-00-57-38.jpg' '.../하나사키워크스프링 히카리/Artemis_2026-09-08-00-57-26.jpg' '.../하나사키워크스프링 히카리/Artemis_2026-09-07-02-36-38.jpg' '.../하나사키워크스프링 히카리/Artemis_2026-09-07-02-36-52.jpg' '.../하나사키워크스프링 히카리/Artemis_2026-09-08-01-58-14.jpg' Data points: moved to gist due to formatting bugs submitted by /u/jupiterbjy to r/LocalLLaMA [link] [comments]

rss:14ebc5ba0d04a9ceUseful?👍👎

93%announcementtechmeme

Anthropic says it's committing 5% of its shares to a Public Benefit Fund, for a total of ~20% with co-founder and staff pledges, plus 2% per 50% valuation rise (Anthropic)

First-party 93%

Abstract

Anthropic : Anthropic says it's committing 5% of its shares to a Public Benefit Fund, for a total of ~20% with co-founder and staff pledges, plus 2% per 50% valuation rise — Today, we're committing a portion of Anthropic's shares to the Anthropic Public Benefit Fund, a new entity with a structure that increases the stake as the company grows.

rss:5bec8baa49e5a8d6Useful?👍👎

92%releasereddit-releases

A Super Mario 64 demake for the GBA built with Claude. Mario’s physics are bit-identical to the original

/u/Nervous_Cold8493

Usable today 92%Measured result 83%

Abstract

I built this with Claude as an experiment: how far can an LLM take a demake if I steer it but don’t write the code? I chose the project, the direction and the trade-offs. Claude wrote the code, the tests and the tooling. Repo: https://github.com/Kodoque1/sm64-gba (MIT, free) What it is SM64’s movement and collision code running under GBA constraints: a 16.8 MHz ARM7 with no FPU. A playable demo: 4 levels, software 3D, Mario animated with the game’s own animation data. An optional libsm64-style import: it reads your ROM to build in Mario’s real model and the real level meshes. The repo contains nothing from Nintendo. How Claude was used My instructions were short (“bit-exact first”, “now optimize”, “make a fluid demo, degrade gracefully”). Claude worked in an agentic session with a toolchain and a headless emulator, profiling, rewriting and checking on its own. The design discussions were the best part. When I asked whether an animated Mario was possible, Claude declined to draw Mario’s likeness but built a wooden mannequin posed by the real animations. I suggested one sprite per body part; it proposed ellipsoids, because an ellipsoid’s outline is always an ellipse, which is exactly what GBA affine sprites can draw. When I suggested a sprite atlas, it explained why that can’t work and proposed rendering the real low-poly mesh, taken from my own ROM. Try it (free) make fetch && make demo builds a ROM that runs in mGBA (mannequin version). With your own ROM: make import ROM=sm64.z64 && make demo make demo-check re-checks the physics. No ROM is distributed. Limits Tested in mGBA only, not on real hardware. 30 fps on 98% of frames. No enemies or objects, flat colors instead of textures. submitted by /u/Nervous_Cold8493 to r/ClaudeAI [link] [comments]

rss:b14b6ea07ccdf2a0Useful?👍👎

90%opinionbluesky362 likes · 22 reposts · 20 replies

A few notes on Anthropic's new Claude Fable 5.1 - with Max thinking level I got the best SVG pelican I've had from any Anthropic model (at…

Simon Willison · @simonwillison.net

Measured result 90%

Abstract

A few notes on Anthropic's new Claude Fable 5.1 - with Max thinking level I got the best SVG pelican I've had from any Anthropic model (at a hefty cost of $3.30!), which I then had it animate simonwillison.net/2026/Sep/1/c...

bsky:1efc4297b248e95cUseful?👍👎

90%releasereleases-ollama

ollama v0.40.6

github-actions[bot]

First-party 90%Usable today 74%

Abstract

What's Changed win: prevent orphaned runners by @dhiltgen in #18500 gguf: add the q2_0 tensor and file types by @jmorganca in #18967 Launch clef in Ollama's cloud by @jberg5 in #19036 use identifiers for --ask mode question keys by @jberg5 in #19041 Full Changelog : v0.40.5...v0.40.6-rc0

Same story elsewhere (3)

ollama v0.40.7 releases-ollama
ollama v0.40.5-rc2 releases-ollama
ollama v0.40.4-rc2 releases-ollama

rss:6966c6e977e61ca9Useful?👍👎

Maybe 80% to 90%

89%releasereddit-ai

Google Open Sources Harness2 for Self Improving AI Agents

/u/chemist_slime

Usable today 89%First-party 57%Measured result 35%

Abstract

Google just released Harness2 as open source. It turns any AI harness into a system that improves itself over time. A harness is everything around the model. System prompt, tools, memory, skills, and subagents. Normally a person designs it once and the agent uses that fixed setup for every new task. With Harness2 the agent itself refines the harness for each task. It proposes changes, runs a few candidate versions, and picks a better one by comparing how they perform. No correct answers or graders are needed. The model weights stay the same. What it learns on one task carries over to the next. After finishing a task it saves the improved harness and useful lessons so future edits start smarter instead of from scratch. Google tested it with Gemini 3.5 Flash and GLM-5 on legal, office, and professional work benchmarks. Even one round of edits beat the original harnesses. More rounds pushed scores higher. GitHub https://github.com/google-research/harness2 submitted by /u/chemist_slime to r/LocalLLaMA [link] [comments]

rss:b82c07b7a3703576Useful?👍👎

89%announcementbluesky115 likes · 15 reposts · 5 replies

Our team just shipped Fugu-Ultra v1.1! 🐡 By dynamically orchestrating the latest frontier models, we pushed performance up by 7.9 points. W…

hardmaru · @hardmaru.bsky.social

Measured result 89%First-party 82%Usable today 41%

Abstract

Our team just shipped Fugu-Ultra v1.1! 🐡 By dynamically orchestrating the latest frontier models, we pushed performance up by 7.9 points. We are now beating Fable 5 in complex coding and reasoning tasks without even having Fable 5 in our agent pool. Collective intelligence is the future.

bsky:b8607daedc3b4280Useful?👍👎

89%businesshn

DeepSeek search and fetch – $0.75 per 1000 successful requests

fagnerbrack

Measured result 89%Usable today 74%First-party 70%

Abstract

Article URL: https://api-docs.deepseek.com/guides/search/ Comments URL: https://news.ycombinator.com/item?id=50138531 Points: 567 # Comments: 266

rss:24c093fb31e9979fUseful?👍👎

89%releasereleases-transformers

Release 5.18.0

vasqu

First-party 89%Usable today 83%

Abstract

New Model additions Nemotron 3 Diarization Nemotron 3 Diarization is an open-weight streaming speaker diarization model designed to determine "who spoke when" in real-world audio. It supports both streaming and offline inference, handles up to eight speakers, and orders speaker outputs by each speaker's first arrival in the input audio. The model uses the Arrival-Order Speaker Cache (AOSC) 1 and FIFO queue introduced for Streaming Sortformer 1 , 2 . A single checkpoint supports configurable latency profiles, from an 80 ms input buffer to a 30.4 s offline-style buffer, and configurable output frame resolution in multiples of 10 ms. With chunked inference, the maximum audio duration is not limited. Links: Documentation Add Nemotron3Diarization ( #49056 ) by @eustlb in #49056 NemotronH Omni NemotronH Omni is a multimodal reasoning model from NVIDIA that pairs the NemotronH hybrid Mamba-Transformer language model with a RADIO vision encoder and an optional Parakeet-based sound encoder. Image (and video) patches are projected through a RADIO tower and a pixel-shuffle MLP into the language model's embedding space at the <image> / <video> context-token positions; audio clips are projected in the same way at <audio> positions. The result is a single autoregressive model that reasons jointly over text, images, video and sound. Links: Documentation Add support for Nemotron Omni ( #46509 ) by @meatybobby in #46509 HyperCLOVAX Vision V2 HyperCLOVAX Vision V2 is a multimodal vision-language model developed by NAVER. It combines the HyperClovaX language model backbone with a Qwen2.5-VL vision encoder. The model supports text, image, and video inputs and is capable of chain-of-thought reasoning via built-in thinking tokens ( <think>...</think> ). Links: Documentation add HyperClovaX Vision ( #44314 ) by @jp1924 in #44314 GTE GTE was proposed in mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval by Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li and Min Zhang. GTE is a BERT-style bidirectional encoder that replaces absolute position embeddings with RoPE, uses a gated MLP, and applies layer normalization after each residual connection. The same architecture backs Alibaba's gte-*-v1.5 , gte-multilingual-* and gte-en-mlm-* checkpoints as well as Snowflake's snowflake-arctic-embed-m-v2.0 . Links: Documentation model: Add GTE to Transformers ( #48416 ) by @harshaljanjani in #48416 Breaking changes 🚨 [ROCm] gpt-oss: route FA3 to aiter-flash-attn, generate ROCm fixtures ( #46837 ) by @Abdennacer-Badaoui 🚨 Speed up detr image processing ( #48066 ) by @guarin 🚨 Remap indexers layer_type ( #48974 ) by @Cyrilvallez 🚨 [vLLM] Fix video token counting for Transformers backend video inputs (Part 1) ( #48894 ) by @harshaljanjani 🚨 🚨Bring some dinos to modern standards ( #46266 ) by @molbap 🚨 [ Kernels ] Bump version ( #48714 ) by @vasqu Bugfixes and improvements fix incorrect hub tokenizer class ( #48641 ) by @itazap [CI] Deduplicate Nvidia/AMD CI reply comments and add headers ( #48655 ) by @ydshieh Update dev ( #48654 ) by @vasqu [tests] Fix NougatModelIntegrationTest: pin artifact revision and update golden values ( #48638 ) by @ydshieh [fix] Fix GlmOcr integration tests: wrong token IDs and image token decode bug ( #48650 ) by @ydshieh [GLM 5.3 Flash] Preserve original names when saving text checkpoints ( #48676 ) by @Dovis01 Register activation kernel layers on XPU ( #47858 ) by @jiqing-feng Mirror the registered pytree flatten when building automatic dynamic shapes ( #48578 ) by @IlyasMoutawwakil Fix AutoImageProcessor requiring torchvision when only Pillow is installed ( #48616 ) by @blipbyte Assert cached decode matches recomputing without a cache ( #48289 ) by @IlyasMoutawwakil [higgs_audio_v2] Fix: use config.num_codebooks in audio labels tensor (Simple Fix!) ( #48560 ) by @b-re-w [ MoE ] Fix eager EP ( #48653 ) by @vasqu [MusicgenMelody] Fix conditioning silently dropped at generation step 0 ( #48679 ) by @ydshieh Fix deepspeed ci ( #48640 ) by @SunMarc Synthetic test assets ( #48589 ) by @tarekziade Skip the expert-parallel sentinel masking when expert parallelism is off ( #48201 ) by @qgallouedec [Fix] yolos offload issue ( #48688 ) by @molbap [serge] Fix 2 integration tests for model minimax failing with output_mismatch (tensor values differ (2)) ( #48515 ) by @sergereview[bot] [serge] Fix 2 integration tests for model mistral failing with other (other (2)) ( #48429 ) by @sergereview[bot] Fix Qwen2.5-VL temporal RoPE for fractional video intervals ( #48669 ) by @yeyeyeping Fix slow integration tests on XPU ( #48611 ) by @jiqing-feng QA: Added a MemoryCleanupMixin class for tests ( #48681 ) by @tarekziade [serge] Fix 1 integration test for model flex_olmo failing with other (other (1)) ( #48668 ) by @sergereview[bot] Fix D-FINE / RT-DETR main loss being computed over the denoising queries ( #48528 ) by @stefan-it [CI] Replace hardcoded username allowlists with author_association check in workflow triggers ( #48712 ) by @ydshieh Add flash_attention_4 in attn_implementation AutoModel docstring ( #48684 ) by @3manifold Keep the expert-parallel sentinel slots out of the router gradient ( #48689 ) by @qgallouedec [DeepseekV3] OOM cascade root-cause investigation (generator ref leak in conversion_mapping) ( #48720 ) by @ydshieh Use huggingface_hub httpx export ( #48685 ) by @Wauplin unifying device_mesh init to enable PP + TP inference ( #48155 ) by @3outeille Enable FSDP2 + expert parallelism via a 2-D (fsdp, tp) device mesh ( #48516 ) by @qgallouedec Switch daily CI to torch 2.14 — update expected outputs ( #48750 ) by @ydshieh fix videomae load error ( #48675 ) by @sywangyi Fix silently random-initializing RTDetrModel / SEWDForCTC loads (wrong base_model_prefix ) ( #48744 ) by @ Fix Glm4vMoeIntegrationTest: offload_folder + MemoryCleanupMixin ( #48776 ) by @ydshieh [InternVL] Normalize num_patches before np.cumsum ( #48469 ) by @lorenzozanee Kernel api doc, part 2 ( #46889 ) by @michaelbenayoun Fix hidden state selection on Gemma4 assistant's first prefill step ( #48704 ) by @glistening Fix inkling embedding norm ( #48786 ) by @Cyrilvallez Fix TextToAudioPipeline crash for tokenizer-only models ( #48505 ) by @jiqing-feng Cast pixel values to the patch embedding dtype in DeepSeek-OCR-2 ( #48632 ) by @jiqing-feng add kernel mapping entry for RMSNormGated, KDA, Conv1D on XPU ( #48702 ) by @kaixuanliu Bump peft version requirement ( #48716 ) by @shniubobo [serge] Fix 12 integration tests for model edgetam failing with import_or_config (other (12)) ( #48322 ) by @sergereview[bot] Restore legacy tensor-parallel initialization for compatibility ( #48797 ) by @3outeille Fix kosmos flaky test ( #48800 ) by @IlyasMoutawwakil Add integration tests for MuseGlimmerAssistantModel ( #48796 ) by @ydshieh Fix AutoModel.from_pretrained not restoring modules_to_save weights ( #48595 ) by @shniubobo [docs] mrope and axial rope ( #48717 ) by @stevhliu Fix decoder only path for older bert variants ( #48785 ) by @Cyrilvallez [Fix] Clean-up ternaries in the DeepSeek family ( #48447 ) by @remi-or [utils] Add MUSA support for Flash Attention 2 ( #48612 ) by @XiaomingFun233 [generate] Drop attention mask early without padding ( #48814 ) by @Cyrilvallez Pin utf-8 in test_can_init_all_missing_weights source read (Windows non-UTF-8 locale fix) ( #48819 ) by @dltsum Relax static-cache tolerance in test_generate_with_static_cache (1e-5 → 5e-5) ( #48815 ) by @ydshieh Detect nested rope_parameters without relying on layer_types ( #48798 ) by @hmellor Support for MoE in the GGUF integration ( #48529 ) by @SunMarc [docs] gguf ( #46357 ) by @stevhliu [apply_chat_template] pass sampling_rate to call ( #48794 ) by @eustlb [CI] Add link checker ( #48160 ) by @stevhliu Qwen3.8 GGUF ( #48660 ) by @SunMarc docs: fix broken #combining-with-fsdp2 anchor in expert_parallelism.md ( #48854 ) by @ydshieh [GPTNeoXJapanese] Fix RoPE ignoring partial_rotary_factor ( #48652 ) by @blipbyte QA: deactivate rule 41 ( #48852 ) by @tarekziade Fix reset on the dynamic cache layers ( #48809 ) by @jiqing-feng [generate] Make all methods and logits processors agnostic to lm_head output size ( #48846 ) by @Cyrilvallez Fix kosmos ( #48853 ) by @Cyrilvallez Add test for causal only variant of some encoder-decoder models ( #48760 ) by @nandan2003 Fix ESMFold2 ligand iPLDDT weighting: mol_type non-polymer code is 3, not 4 ( #48831 ) by @faustomilletari Fix flaky RfDetr test_save_load ( #48869 ) by @ydshieh fix(ci): harden GitHub Actions workflows ( #48160 ) ( #48834 ) by @hf-security-analysis[bot] Fix Glm4MoeIntegrationTest: split into 3 tests, switch to GLM-4.5-Air ( #48820 ) by @ydshieh Fix StaticCache for Mllama and enable torch.compile ( #48141 ) by @jiqing-feng Fix Minimax M2 partial_rotary_factor by remapping legacy rotary_dim ( #48486 ) by @dajiaohuang Fix Pixtral image processor do_pad option ( #48872 ) by @jzakrzew [CB] Add pause mechanism ( #48462 ) by @remi-or Fix DFlash sampled candidates losing the batch dimension ( #48281 ) by @VaggelisGian Fix sliding window cache when assistant model calls generate ( #48280 ) by @VaggelisGian Fix GLM rope_parameters dict shared mutation across test instances ( #48895 ) by @ydshieh [ feat ] Allow untying hidden_states[-1] from last_hidden_state via the model config ( #48087 ) by @tomaarsen Fix Idefics2 padding when the first example has no image ( #48753 ) by @Arnavsharma2 Don't fail model loading when the accelerator can't report free memory ( #48136 ) by @studioego [Improvement] Rework the docstring and comments of mHC ( #48888 ) by @remi-or [serge] Fix 3 integration tests for model deepseek_vl failing with other (other (3)) ( #48536 ) by @sergereview[bot] Add auto_docstring task model overrides ( #47815 ) by @guarin Make LongcatFlashConfig self-consistent without building the model ( #48899 ) by @hmellor [ DSA ] Only save latents on dsa with indexer as well ( #48876 ) by @vasqu [Chat Parsing] Coerce oneOf tool arguments ( #48719 ) by @yonigozlan Always materialize the causal mask in Doge so sdpa stays causal ( #48821 ) by @blipbyte Fix docstring argument names that don't match signatures ( #48474 ) by @IshaanPotle Mark test_generate_with_static_cache as flaky for olmo and bigbird_pegasus ( #48856 ) by @ydshieh Clean up some MoE models' integration tests ( #48833 ) by @ydshieh Fix T5 tied weights order for lm_head ( #48238 ) by @vaibhavmashal Size the device_map buffer from the largest leaf module ( #47211 ) by @dhruv7477 Fix compile cache tests ( #48923 ) by @Cyrilvallez Update arXiv citation in NeoMME model doc ( #48926 ) by @tonywu71 Standardise Aria's MoE onto the experts interface ( #48907 ) by @hmellor Fix possessive typo in Whisper long-form warning ( #48877 ) by @erikdw Modular conversion small fix ( #48934 ) by @zucchini-nlp fix(vibevoice-asr): use integer ceiling division for audio token count ( #48864 ) by @ege-arhan Improve lazy import error messages ( #48602 ) by @karatarassul4-max Fix tests due to dropping attn mask ( #48903 ) by @SunMarc docs: fix docstring parameters that do not match signatures ( #48904 ) by @simpleqt DOC: Improve documentation for remap_legacy_layer_types function ( #48651 ) by @mathewOracle Align special tokens on the text config ( #48847 ) by @qgallouedec docs: fix dead doc links in i18n READMEs and the xlnet docstring ( #48905 ) by @simpleqt Fix AXK2 integration test: update CUDA expected text and rename class ( #48941 ) by @ydshieh Resolve the Hub revision once per load instead of passing a private _commit_hash around ( #47611 ) by @Wauplin Keep image processor backends in sync on keys and dtypes ( #48739 ) by @yupengtang Fix infeasible cost matrix errors in hugarian matcher losses ( #47730 ) by @guarin Fix more stuff ( #48979 ) by @zucchini-nlp Skip an unnecessary image copy in the torchvision image normalization path ( #48897 ) by @jzakrzew Better attn default for gguf ( #48935 ) by @SunMarc Add pose estimation keypoint preprocessing to Sapiens2ImageProcessor ( #47199 ) by @Sainava Fix image processor class-level size mutation and min_pixels handling ( #48916 ) by @Gracy769 Contract the mamba2 chunk scan with einsum instead of broadcast-then-sum ( #48978 ) by @tarekziade Fix typos in DeepseekV4 comments ( #48953 ) by @Janiarafath Reduce peak memory in examples_torch CI job (OOM fix) ( #48983 ) by @ydshieh Fix vibevoice TTS batched audio index ( #48902 ) by @ebezzam [CB] [Major] Upgrade the cache to support different attention types ( #47809 ) by @remi-or Fix SwitchTransformers Top1 router: raw logits, expert capacity accounting, and router losses ( #48421 ) by @yurekami Fix Qwen3OmniMoeIntegrationTest OOM ( #48987 ) by @ydshieh Let prefix_allowed_tokens_fn override model -inf and raise an exception on unsatisfiable generation constraints. ( #48927 ) by @ksh108405 [generate] Simplify candidate generators by removing required update_candidate_strategy ( #48982 ) by @Cyrilvallez [generate] Always correctly restrict assisted decoding with max length/eos token ( #48981 ) by @Cyrilvallez QA: applied ruff rule PLW1514 ( #48990 ) by @tarekziade Update tokenizer gguf support ( #48656 ) by @SunMarc [vLLM] Fix video token counting for Transformers backend video inputs (Part 2) ( #48900 ) by @harshaljanjani Update ggml kernels path ( #48991 ) by @SunMarc Enable compressed-tensors FP8 kernels on MPS (torch >= 2.15) ( #48985 ) by @Isalia20 Fix mps autocast handling in rotary embeddings ( #49006 ) by @Isalia20 Fix static cache per layer head shapes ( #48619 ) by @dacorvo Summarization examples: download NLTK punkt_tab, not punkt ( #49014 ) by @davanstrien [docs] Fix legacy hf CLI references (transformers) ( #48988 ) by @Wauplin Fix RecurrentGemma compiled generation with StaticCache ( #48961 ) by @sywangyi updated GraniteMoeHybrid expectations (CPU) ( #49016 ) by @tarekziade OpenVINO HF Exporter ( #47003 ) by @IlyasMoutawwakil fix noisy comments ( #49013 ) by @tarekziade Fix assisted decoding for VLM due to dropping attn mask ( #49019 ) by @SunMarc Deprecate min-max pixels ( #49021 ) by @zucchini-nlp Keep special token ids the tokenizer does not define ( #48708 ) by @albertvillanova Added more usage of MemoryCleanupMixin ( #49011 ) by @tarekziade Shorten noisy OpenVINO SDPA comment ( #49033 ) by @tarekziade PEFT x Dtensor-based TP integration ( #48485 ) by @michaelbenayoun fix(zamba): add use_associative_scan config flag to avoid torch.compile slowdown ( #48331 ) by @msnliu Scope GITHUB_TOKEN permissions per job ( #49046 ) by @hf-security-analysis[bot] Fix mask creation not being skipped under torch.compile ( #48975 ) by @jiqing-feng [Chat] Loading GGUF models served with the Chat CLI ( #49031 ) by @ariG23498 Pin GitHub Actions to commit SHAs ( #49049 ) by @hf-security-analysis[bot] Only seed numpy in BigBird block-sparse attention during training ( #49037 ) by @jiqing-feng QA: Fix llama4 leak ( #49042 ) by @tarekziade Fix startup failures: drop permissions reusable-workflow callers cannot grant ( #49055 ) by @paulinebm Fix startup failures: drop pull-requests: read from check_failed_tests.yml ( #49057 ) by @paulinebm Register the remaining mamba-ssm kernel layers on XPU ( #49035 ) by @jiqing-feng Add workflow for building XPU CI Docker images ( #49039 ) by @regisss Fix assistant masks for processors ( #48793 ) by @Rocketknight1 Add MPS maintainer ( #49052 ) by @Rocketknight1 [docs] Loading behavior ( #49023 ) by @stevhliu [Nemotron3Diarization] nit: hub pr merged to main ( #49065 ) by @eustlb fix(ci): harden GitHub Actions workflows ( #49057 ) ( #49059 ) by @hf-security-analysis[bot] Honor config.output_router_logits in the MoE VLM wrappers ( #48885 ) by @qgallouedec Fix XPU Docker image build ( #49073 ) by @regisss Fix assisted eos token condition ( #49075 ) by @Cyrilvallez [serge] Fix OOM in Moshi integration tests with MemoryCleanupMixin ( #48839 ) by @sergereview[bot] Fix odd head_dim validation for RoPE configurations ( #48524 ) by @somuai Keep already-decoded array arguments unchanged ( #49062 ) by @yonigozlan Fix NaN in Parakeet eager attention with padded batches ( #49070 ) by @ArthurZucker Bump huggingface_hub upper bound to <3.0 (transformers) ( #49083 ) by @Wauplin Keep attention_mask as None in OPT's causal mask creation ( #49002 ) by @jiqing-feng Add Trainer.end ( #48875 ) by @qgallouedec Add image processing tester init ( #48829 ) by @guarin [Executorch] Add MLX recipe ( #48910 ) by @metascroy [Zamba] Fix associative scan breaking ONNX export and OOM in integration test ( #49092 ) by @ydshieh Fix RGB early-return skipping PNG tRNS compositing ( #49005 ) by @cs-fisha Use namespaced dataset ids in docs and PyTorch examples ( #49017 ) by @davanstrien Fix MaskFormerSwin attention mask dtype to follow hidden states ( #49032 ) by @kaixuanliu [NemotronH-Omni] Fix device mismatch in test tensor creation ( #49102 ) by @ydshieh [serge] Fix 2 integration tests for model cvt failing with output_mismatch (tensor values differ (2)) ( #49076 ) by @sergereview[bot] [serge] Fix 2 integration tests for model hy_v3 failing with output_mismatch (tensor values differ (2)) ( #49068 ) by @sergereview[bot] [serge] Fix 2 integration tests for model pvt_v2 failing with other ( #49067 ) by @sergereview[bot] Keep the eos ids the config declares ( #49082 ) by @albertvillanova Use grouped_mm on TPU devices under torch.compile ( #49097 ) by @salkan0 [serge] Fix 2 integration tests for model jamba failing with other (other (2)) ( #49044 ) by @sergereview[bot] Remap the legacy Gemma 1 hidden_act in the config post-init ( #49084 ) by @PCfVW Fix exporters import on torch < 2.9 (is_contiguous_or_false) ( #49124 ) by @ydshieh [gemma3n] fix audio test fixture: use hf_hub_download instead of load_dataset ( #49128 ) by @ydshieh qwen3 models map to wrong tokenizer class on the hub ( #49116 ) by @itazap Restore the Unicode whitespace set in the GPT-SW3 tokenizer ( #48912 ) by @David-Wu1119 [AMD] Fix some integration tests ( #49153 ) by @Abdennacer-Badaoui [Gemma] Fix test_model_7b_fp16_static_cache expected value for cuda 8 after #49084 ( #49133 ) by @ydshieh Fix UMT5 decoder self-attention not being causal ( #49135 ) by @the-cross-art Skip flash tests that fall back to a hub kernel when kernels is missing ( #49129 ) by @Abdennacer-Badaoui Auto generate model inits ( #47829 ) by @guarin Fix get_json_schema dropping items/enum for unions of list/dict/Literal types ( #49136 ) by @JoeyTan21 Pick the default flash implementation based on the current hardware ( #49109 ) by @Abdennacer-Badaoui Deprecate the use_mamba_kernels config flag that no longer has any effect ( #49155 ) by @albertvillanova [CI] ssh-runner: add optional cache_type input to switch between bucket and EFS runners ( #49159 ) by @ydshieh [generation] Encode multimodal data only once ( #45783 ) by @zucchini-nlp No more -hf repo names for ESMC ( #49158 ) by @Rocketknight1 [MPS] Remove cu_seqlens_k clone workaround for metal-flash-sdpa ( #49091 ) by @Isalia20 [PerceptionLM] Restore test_inputs_embeds overrides to fix flaky test ( #49165 ) by @ydshieh [CB] Fix failing tests discovered when using the B200 ( #49171 ) by @remi-or [imagegpt/vilt/trocr] fix cache fixture tests: use hf_hub_download instead of load_dataset ( #49178 ) by @ydshieh Fix lost call ( #49163 ) by @molbap Document image_like_kwargs ( #49180 ) by @guarin [CacheHardIntegrationTest] use dedicated safetensors repo to fix Xet bucket cache corruption ( #49182 ) by @ydshieh Document running the example scripts on Hugging Face Jobs ( #49050 ) by @davanstrien Fix backslash handling in generate flag values in transformers chat ( #48709 ) by @JHC56 Finish removing the MPS autocast workaround ( #49157 ) by @rubenG1009 Fix Trainer checkpoint resume crashing on CPU with multiple processes ( #49123 ) by @neevmodh Fix command syntax for optimum-cli export ( #46451 ) by @Ahwar QA: fix noisy comment checker ( #49184 ) by @tarekziade update to torch 2.14 ( #49199 ) by @sywangyi Video processors - general maintenance ( #48251 ) by @zucchini-nlp [Parakeet] Convert NeMo's stochastic depth to layerdrop ( #49191 ) by @Deep-unlearning [MPS] Let mps sdpa handle grouped query attention directly ( #49187 ) by @Isalia20 Replace datasets that no longer load with maintained uploads ( #49018 ) by @davanstrien Fail fast on eval OOM under auto_find_batch_size ( #49198 ) by @qgallouedec Fix SequenceBiasLogitsProcessor edge cases: token id 0 and prefix equal to context ( #49117 ) by @lucaluo925 Map bare list, tuple and dict annotations to the right JSON schema type in get_json_schema ( #49145 ) by @825pranav [ Kernels ] Sync mamba version ( #49205 ) by @vasqu gguf user defined tokens ( #49008 ) by @SunMarc QA: restore masking_utils export comment with a noqa ( #49209 ) by @tarekziade Fix deepstack features for mixed-input ( #49177 ) by @zucchini-nlp Fix stale _added_tokens_encoder entries in cpmant and wav2vec2 ( #47440 ) by @ishan-1010 Fix additional_special_tokens data loss with extra_special_tokens ( #47848 ) by @erichanwang Fix MPS GQA version gating ( #49210 ) by @Isalia20 Add Strix Halo (gfx1151) Atlas Inference Hub-kernel path for Qwen3.5/3.6/3.8 Gated DeltaNet ( #49127 ) by @AzeezIsh Fix MiniMax M3 partial 3D vision rotary embeddings ( #49164 ) by @cuichenx Fix missing router_logits in Qwen3.5-MoE and other MoE models ( #49179 ) by @zucchini-nlp [Nemotron3Diarization] fix streaming last stft frame dropped ( #49167 ) by @eustlb Significant community contributions The following contributors have made significant changes to the library over the last release: @harshaljanjani model: Add GTE to Transformers ( #48416 ) [vLLM] Fix video token counting for Transformers backend video inputs (Part 2) ( #48900 ) 🚨 [vLLM] Fix video token counting for Transformers backend video inputs (Part 1) ( #48894 ) @eustlb [Nemotron3Diarization] fix streaming last stft frame dropped ( #49167 ) [Nemotron3Diarization] nit: hub pr merged to main ( #49065 ) Add Nemotron3Diarization ( #49056 ) [apply_chat_template] pass sampling_rate to call ( #48794 ) @tarekziade QA: restore masking_utils export comment with a noqa ( #49209 ) QA: fix noisy comment checker ( #49184 ) QA: Fix llama4 leak ( #49042 ) Shorten noisy OpenVINO SDPA comment ( #49033 ) Added more usage of MemoryCleanupMixin ( #49011 ) fix noisy comments ( #49013 ) updated GraniteMoeHybrid expectations (CPU) ( #49016 ) QA: applied ruff rule PLW1514 ( #48990 ) Contract the mamba2 chunk scan with einsum instead of broadcast-then-sum ( #48978 ) QA: deactivate rule 41 ( #48852 ) QA: Added a MemoryCleanupMixin class for tests ( #48681 ) Synthetic test assets ( #48589 ) @ydshieh [CacheHardIntegrationTest] use dedicated safetensors repo to fix Xet bucket cache corruption ( #49182 ) [imagegpt/vilt/trocr] fix cache fixture tests: use hf_hub_download instead of load_dataset ( #49178 ) [PerceptionLM] Restore test_inputs_embeds overrides to fix flaky test ( #49165 ) [CI] ssh-runner: add optional cache_type input to switch between bucket and EFS runners ( #49159 ) [Gemma] Fix test_model_7b_fp16_static_cache expected value for cuda 8 after #49084 ( #49133 ) [gemma3n] fix audio test fixture: use hf_hub_download instead of load_dataset ( #49128 ) Fix exporters import on torch < 2.9 (is_contiguous_or_false) ( #49124 ) [NemotronH-Omni] Fix device mismatch in test tensor creation ( #49102 ) [Zamba] Fix associative scan breaking ONNX export and OOM in integration test ( #49092 ) Fix Qwen3OmniMoeIntegrationTest OOM ( #48987 ) Reduce peak memory in examples_torch CI job (OOM fix) ( #48983 ) Fix AXK2 integration test: update CUDA expected text and rename class ( #48941 ) Clean up some MoE models' integration tests ( #48833 ) Mark test_generate_with_static_cache as flaky for olmo and bigbird_pegasus ( #48856 ) Fix GLM rope_parameters dict shared mutation across test instances ( #48895 ) Fix Glm4MoeIntegrationTest: split into 3 tests, switch to GLM-4.5-Air ( #48820 ) Fix flaky RfDetr test_save_load ( #48869 ) docs: fix broken #combining-with-fsdp2 anchor in expert_parallelism.md ( #48854 ) Relax static-cache tolerance in test_generate_with_static_cache (1e-5 → 5e-5) ( #48815 ) Add integration tests for MuseGlimmerAssistantModel ( #48796 ) Fix Glm4vMoeIntegrationTest: offload_folder + MemoryCleanupMixin ( #48776 ) Switch daily CI to torch 2.14 — update expected outputs ( #48750 ) [DeepseekV3] OOM cascade root-cause investigation (generator ref leak in conversion_mapping) ( #48720 ) [CI] Replace hardcoded username allowlists with author_association check in workflow triggers ( #48712 ) [MusicgenMelody] Fix conditioning silently dropped at generation step 0 ( #48679 ) [fix] Fix GlmOcr integration tests: wrong token IDs and image token decode bug ( #48650 ) [tests] Fix NougatModelIntegrationTest: pin artifact revision and update golden values ( #48638 ) [CI] Deduplicate Nvidia/AMD CI reply comments and add headers ( #48655 ) @molbap Fix lost call ( #49163 ) 🚨 🚨Bring some dinos to modern standards ( #46266 ) [Fix] yolos offload issue ( #48688 ) @remi-or [CB] Fix failing tests discovered when using the B200 ( #49171 ) [CB] [Major] Upgrade the cache to support different attention types ( #47809 ) [Improvement] Rework the docstring and comments of mHC ( #48888 ) [CB] Add pause mechanism ( #48462 ) [Fix] Clean-up ternaries in the DeepSeek family ( #48447 ) @jiqing-feng Keep attention_mask as None in OPT's causal mask creation ( #49002 ) Register the remaining mamba-ssm kernel layers on XPU ( #49035 ) Only seed numpy in BigBird block-sparse attention during training ( #49037 ) Fix mask creation not being skipped under torch.compile ( #48975 ) Fix StaticCache for Mllama and enable torch.compile ( #48141 ) Fix reset on the dynamic cache layers ( #48809 ) Cast pixel values to the patch embedding dtype in DeepSeek-OCR-2 ( #48632 ) Fix TextToAudioPipeline crash for tokenizer-only models ( #48505 ) Fix slow integration tests on XPU ( #48611 ) Register activation kernel layers on XPU ( #47858 ) @Wauplin Bump huggingface_hub upper bound to <3.0 (transformers) ( #49083 ) [docs] Fix legacy hf CLI references (transformers) ( #48988 ) Resolve the Hub revision once per load instead of passing a private _commit_hash around ( #47611 ) Use huggingface_hub httpx export ( #48685 ) @meatybobby Add support for Nemotron Omni ( #46509 ) @Sainava Add pose estimation keypoint preprocessing to Sapiens2ImageProcessor ( #47199 ) @jp1924 add HyperClovaX Vision ( #44314 )

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88%measurementreddit-ai

Six frontier AIs competed to design and 3D-print egg-protecting capsules. Claude Opus 5.5's egg survived a 20-foot drop without a crack. GPT-6 Astra failed at 8 feet, while Gemini 4 Argon, Grok 4.7, Qwen 3.8-Max, and Muse Wonder failed at 4 feet. Claude dominates another real-world engineering chall

/u/141_1337

Measured result 88%

Abstract

submitted by /u/141_1337 to r/singularity [link] [comments]

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88%announcementbluesky15 likes · 1 repost · 0 replies

We just pushed a big update to Sakana Chat Free to use: chat.sakana.ai A big motivation for this release is getting people in Japan, especi…

hardmaru · @hardmaru.bsky.social

Usable today 88%First-party 84%

Abstract

We just pushed a big update to Sakana Chat Free to use: chat.sakana.ai A big motivation for this release is getting people in Japan, especially kids, excited about software development. Vibe-coding turns Kanji & Math drills during the summer break into fun games they can actually build themselves.

bsky:18eeb3307e6e5874Useful?👍👎

88%releasereleases-llama-cpp

llama.cpp b11722

github-actions[bot]

First-party 88%Usable today 83%

Abstract

hexagon: allow multistream FA ( #30218 ) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/57232684 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

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88%releasehuggingface-blog

Google releases Gemma 2 2B, ShieldGemma and Gemma Scope

First-party 88%Usable today 65%

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87%releasereddit-releases

Rebuilt a 2000 RTS from scratch with Claude Code, working from the original's disassembly

/u/ColCarlo99

Usable today 87%

Abstract

I used Claude Code to remake Tzar: The Burden of the Crown (2000) from scratch in Rust + Bevy. The workflow that worked for me: Claude reads the disassembly of the original exe and ports one system at a time: combat, AI, map generator, mission scripts I play it next to the original and report anything that feels off; Claude traces it back to the original code 600+ tests and memory notes between sessions keep long work from drifting It plays the original campaigns and random maps on Windows 10/11, up to 4K. Free and open source (needs any copy of Tzar): https://github.com/ColCarlo/ReTzar Happy to answer questions about the setup. submitted by /u/ColCarlo99 to r/ClaudeAI [link] [comments]

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87%releasebluesky42 likes · 8 reposts · 3 replies

Datasette 1.0a39 and 0.65.4 security releases - Datasette Blog

Simon Willison · @simonwillison.net, datasette.io · discussion

First-party 87%Usable today 54%

Abstract

Datasette 1.0a39 and 0.65.4 security releases - datasette.io/blog/2026/se... We ran an extensive audit using Claude Fable 5.1, GPT-5.6 Sol and GPT-6 Astra, then fixed a range of different bugs. If you're running a Datasette instance on a public website you should upgrade.

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87%announcementbluesky54 likes · 4 reposts · 0 replies

Incredibly proud of the Sakana AI team. We have developed an orchestration model right here out of Japan that achieves state-of-the-art per…

hardmaru · @hardmaru.bsky.social

First-party 87%Usable today 35%Measured result 34%

Abstract

Incredibly proud of the Sakana AI team. We have developed an orchestration model right here out of Japan that achieves state-of-the-art performance on real-world cybersecurity benchmarks! 🎌 sakana.ai/fugu-cyber-r...

bsky:44e0b5340b6dbe76Useful?👍👎

87%releasereleases-llama-cpp

llama.cpp b11723

github-actions[bot]

First-party 87%Usable today 84%

Abstract

hexagon: FLASH_ATTN_EXT improvements for longer context and SWA models ( #30731 ) hex-fa: support for masked block skipping to reduce DMA fetching hex-fa: finetuning the hmx->hvx crossover thresholds (boosting gemma-4) hex-fa: update hmx gate to account for mdev.count Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/57234765 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

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87%releasereleases-transformers

Release 5.17.0

vasqu

First-party 87%Usable today 86%Measured result 75%

Abstract

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. The architecture combines four features: Multi-head Latent Attention (MLA) compresses keys and values into a low-rank latent ( kv_lora_rank ) that kv_b_proj expands back to one key/value per query head. DeepSeek Sparse Attention (DSA) selects index_topk keys per query with a lightweight indexer. Following IndexShare , only the layers marked "full" in indexer_types run an indexer; "shared" layers reuse the previous full layer's selection. Gated MLA with learnable attention sinks , where each head owns a sink logit that participates in the softmax and contributes no value, as in GPT-OSS . Independent Hyper-Connections (iHC) replace the plain residual path with hc_mult parallel residual streams that are collapsed before, and redistributed after, every sublayer. The implementation does not execute the multi-token prediction (MTP) layers. Released checkpoints keep those weights so that other runtimes can use them for speculative decoding; they are ignored at load time. Links: Documentation Add h4 ( #48473 ) by @ArthurZucker in #48473 VibeVoice VibeVoice is a novel framework for synthesizing high-fidelity, long-form speech with multiple speakers by employing a next-token diffusion approach within a Large Language Model (LLM) structure. It's designed to capture the authentic conversational "vibe" and is particularly suited for generating audio content like podcasts and multi-participant audiobooks. Links: Documentation Implement VibeVoice ( #40546 ) by @pengzhiliang in #40546 NeoMME NeoMME is a family of efficient 260M and 800M parameter multimodal-native multilingual foundation encoders from H Company. It processes multilingual text tokens and raw image patches in a single bidirectional Transformer encoder, without a separately pretrained vision tower or causal language model. NeoMME-Retriever is a model fine-tuned from the NeoMME backbone for visual document retrieval with joint late-interaction and dense objectives. It takes text queries and documents (text or page screenshots) and produces multi-vector embeddings for MeanMaxSim scoring (late-interaction) and mean-pooled embeddings for cosine similarity (dense). Links: Documentation Add NeoMME and NeoMME-Retriever ( #47992 ) by @tonywu71 in #47992 Fun-ASR-Nano Fun-ASR-Nano is an 800M-parameter end-to-end speech recognition model developed by Alibaba DAMO Academy's FunAudioLLM team. It achieves state-of-the-art performance on Chinese, English, and Japanese ASR benchmarks while being significantly smaller than comparable models. Key features are Chinese, English, and Japanese , including 7 Chinese dialects and 26 regional accents Hotword customization for domain-specific vocabulary Native punctuation output (no separate punctuation model needed) Links: Documentation Add Fun-ASR-Nano model ( #46180 ) by @LauraGPT in #46180 KimiLinear Kimi Linear is a hybrid linear attention architecture from Moonshot AI, introduced in Kimi Linear: An Expressive, Efficient Attention Architecture . At its core is Kimi Delta Attention (KDA) , a refinement of Gated DeltaNet that gives each key channel its own forget gate, so the recurrent state decays per channel instead of per head. KDA is used in most layers; every fourth layer keeps a full-attention block that reuses DeepSeek-V3's Multi-head Latent Attention (MLA), and the feed-forward blocks are DeepSeek-V3-style MoE with a shared expert. Links: Documentation Kimi linear ( #48250 ) by @remi-or in #48250 Canary Canary-1B-v2, a fast, robust multilingual model for Automatic Speech Recognition (ASR) and Speech-to-Text Translation (AST): Canary reuses the Fast Conformer encoder from Parakeet (loaded through [ ParakeetEncoder ] / [ ParakeetEncoderConfig ]) and pairs it with a Transformer decoder that uses fixed sinusoidal positional embeddings, cross-attention to the encoder outputs and tied input/output embeddings. The task is selected through a decoder prompt prefix built by [ CanaryProcessor ] of the form <|startofcontext|> <|startoftranscript|> <|emo:undefined|> <source_lang> <target_lang> <pnc|nopnc> <|noitn|> <|notimestamp|> <|nodiarize|> , where source_lang == target_lang selects transcription and otherwise selects translation. Links: Documentation model: Add NVIDIA Canary-1B-v2 to Transformers ( #46825 ) by @harshaljanjani in #46825 NeuCodec The NeuCodec model was proposed in Finite Scalar Quantization Enables Redundant and Transmission-Robust Neural Audio Compression at Low Bit-rates . NeuCodec is a neural audio codec extending on XCodec2. It takes advantage of the following features: Finite Scalar Quantization (FSQ) quantisation resulting in a single codebook , making it ideal for downstream modeling with Speech Language Models. Trained with CC data such that there are no Non-Commercial data restrictions . At 50 tokens/sec and 16 bits per token, the overall bit-rate is 0.8kbps . The codec takes in 16kHz input and outputs 24kHz using an upsampling decoder . The FSQ encoding scheme allows for bit-level error resistance suitable for unreliable and noisy channels. Links: Documentation Add support for NeuCodec ( #47143 ) by @harryjulian in #47143 Breaking changes Vision rotary embeddings (2D/3D) have been standardized into a unified RoPE frequency computation module, so users with custom vision models relying on attention-layer-level or model-specific RoPE grid interleaving logic must migrate to the new centralized modeling_rope_utils.py implementation. 🚨 Vision (2d/3d) rotary embeddings ( #48105 ) by @zucchini-nlp Generation Generation improvements include a performance optimization that avoids unnecessary accelerator synchronization on every decode step (reducing per-step overhead), and a fix to prevent unconditional downloading of remote hub files during generation. Several correctness fixes were also applied, including enforcing auto-compile cache checks for encoder-decoder models, standardizing past_key_values naming in AfMoE, and resolving flaky export and integration test failures. [ Generate ] Avoid unconditionally downloading remote hub file ( #48620 ) by @vasqu in [ #48620 ] [generate] stop synchronizing the accelerator on every decode step ( #47975 ) by @SunMarc in [ #47975 ] [AfMoE] Standardize past_key_values argument naming across forward and generate ( #48430 ) by @shenhuaqingshi in [ #48430 ] Fix MTP generation test regex gate for escaped layer ignore keys ( #48003 ) ( #48262 ) by @Noxtimo in [ #48262 ] fix(generation): Enforce the auto-compile cache check for encoder-decoder models ( #48364 ) by @harshaljanjani in [ #48364 ] [serge] Fix 4 integration tests for model generation failing with output_mismatch (list output differs (4)) ( #48133 ) by @sergereview[bot] in [ #48133 ] [VibeVoice] Skip generate export tests (flaky) ( #48396 ) by @ydshieh in [ #48396 ] Cache Fixed several cache-related bugs, including a quantized cache issue in VibeVoice, incorrect rejection of non-static cache implementations in VoxtralRealtime, missing auto-compile cache checks for encoder-decoder models, and a silent failure when paged attention is called without a cache. Documentation was also updated to clarify ContinuousBatchingConfig usage and sliding window model limitations. vibevoice: fix bug for quant cache ( #48487 ) by @kaixuanliu in [ #48487 ] Fix VoxtralRealtime rejecting non-static cache implementations ( #48082 ) by @jiqing-feng in [ #48082 ] Raise when a paged attention forward is called with no cache ( #48297 ) by @qgallouedec in [ #48297 ] [docs] Pass ContinuousBatchingConfig and sliding window models ( #48381 ) by @stevhliu in [ #48381 ] Retry get_daily_ci_runs on stale GitHub API cache ( #48374 ) by @ydshieh in [ #48374 ] Kernels Kernel support was improved with fixes for nested FLA kernel imports when only fla-core is installed, a warning when hub-kernel functions silently fall back to slower pure-PyTorch reference implementations, and the ability to register standalone functions (e.g., RoPE) in KernelConfig with optional non-inheritance of default mappings. Additional fixes include corrected repository paths for ESMFold2 kernels and updated documentation for KernelConfig customization. Support nested FLA kernel imports for fla-core ( #48221 ) by @DimensionSTP in [ #48221 ] Warn once when a hub-kernel function falls back to its reference PyTorch path ( #48185 ) by @qgallouedec in [ #48185 ] [docs] Kernel updates ( #48465 ) by @stevhliu in [ #48465 ] [ Kernels ] Enable functions into kernels registry and allow non inheritance ( #48443 ) by @vasqu in [ #48443 ] Fix kernel commit and repo paths for ESMFold2 ( #48186 ) by @Rocketknight1 in [ #48186 ] Quantization Fixed several quantization bugs, including a quant cache issue in VibeVoice, incorrect FP8 embedding handling for Qwen models, missing FP8 tensor parallelism layer overrides, and unnecessary MXFP4 weight dequantization on XPU devices. fix qwen4exp-fp8 ple embedding ( #48368 ) by @JJJYmmm in [ #48368 ] Keep MXFP4 weights quantized on XPU when use_kernels is set ( #47923 ) by @jiqing-feng in [ #47923 ] Fix missing FP8 TP layer overrides ( #48343 ) by @changwangss in [ #48343 ] Bugfixes and improvements MRoPE continued ( #48594 ) by @zucchini-nlp in [ #48594 ] [fix] Update stale expected strings in HunYuanVL integration tests ( #48646 ) by @ydshieh in [ #48646 ] [Quantizaiton]support 5/6/7 bits in AutoRound ( #48481 ) by @wenhuach21 in [ #48481 ] [fix] Update stale golden values and fix expected_logits shape in FlavaForPreTraining integration tests ( #48639 ) by @ydshieh in [ #48639 ] Fix YOLOS device mismatch with device_map="auto" ( #46886 ) by @swankystark in [ #46886 ] Honor shift_labels in decoder-only LLM/VLM losses ( #48493 ) by @qgallouedec in [ #48493 ] [KimiLinear] Fix test_cpu_offload: set num_local_experts=4 in model tester ( #48624 ) by @ydshieh in [ #48624 ] [docs] Per-layer config ( #48601 ) by @stevhliu in [ #48601 ] Fix generate_flags parsing in transformers chat ( #48597 ) by @SunMarc in [ #48597 ] [fix] Fix how we read package versions - triggered by torch 2.14+ ( #48615 ) by @ydshieh in [ #48615 ] [Docker] Upgrade CPU torch to <=2.14.0, torchcodec to <=0.16.0 ( #48614 ) by @ydshieh in [ #48614 ] Fix AttributeError in gradient_checkpointing_enable(offload=True) ( #48590 ) by @tarekziade in [ #48590 ] Another day fixing CI ( #48591 ) by @zucchini-nlp in [ #48591 ] esmfold2: keep distogram_head in fp32 as well ( #48488 ) by @kaixuanliu in [ #48488 ] Add supports_context_parallel to PreTrainedModel ( #48442 ) by @qgallouedec in [ #48442 ] [docs] mlinter reference ( #48460 ) by @stevhliu in [ #48460 ] [docs] Add a LiteRT page under community integrations ( #48540 ) by @john-rocky in [ #48540 ] [GLM 5.3 Flash] Fix NaN gradients in chunked KDA ( #48455 ) by @imvladikon in [ #48455 ] [docs] Fix [[autodoc]] directives in ALBERT model documentation ( #48593 ) by @samyuktahegde in [ #48593 ] [Fix] Fix A10 expectations for a test ( #48454 ) by @remi-or in [ #48454 ] Add PR comment CI for AMD (MI300) ( #48065 ) by @ydshieh in [ #48065 ] docs: fix docstring parameter names that do not match signatures ( #48575 ) by @simpleqt in [ #48575 ] docs: remove phantom parameters from docstrings ( #48576 ) by @simpleqt in [ #48576 ] extend some case to xpu as well ( #48502 ) by @sywangyi in [ #48502 ] [serge] Fix 2 integration tests for model glm4_moe failing with OOM (other (2)) ( #48551 ) by @sergereview[bot] in [ #48551 ] [serge] Fix 2 integration tests for model nemotron failing with import_or_config (other (2)) ( #48582 ) by @sergereview[bot] in [ #48582 ] Compress the agent conventions file and document two modular pitfalls ( #48586 ) by @tarekziade in [ #48586 ] Guard against a None video processor class when the backend is unavailable ( #48557 ) by @caiotheodoro in [ #48557 ] [serge] Fix 2 integration tests regressed by commit 83d46aa (PR #47625 ) ( #48580 ) by @sergereview[bot] in [ #48580 ] [nit] use requires_backends ( #47576 ) by @eustlb in [ #47576 ] [serge] Fix 2 integration tests for model kosmos2 failing with import_or_config (other (2)) ( #48552 ) by @sergereview[bot] in [ #48552 ] Fix failing tests for cohere_compass ( #48005 ) by @kaixuanliu in [ #48005 ] [Fix] Use dedicated helpers for DeepGEMM and SonicMoE tests ( #48523 ) by @remi-or in [ #48523 ] CI: Point test fixtures at hf-internal-testing copies we already host ( #48521 ) by @tarekziade in [ #48521 ] fix failed test cases for glm5_next ( #48497 ) by @kaixuanliu in [ #48497 ] Pass kwargs to the Mamba2 mixer in Nemotron-H, Falcon-H1 and Mamba2 ( #48490 ) by @kfastino in [ #48490 ] Retire test_multi_gpu_data_parallel_forward ( #48508 ) by @tarekziade in [ #48508 ] Processing tests [part 2] ( #47922 ) by @zucchini-nlp in [ #47922 ] QA: Add noisy comment checker ( #48484 ) by @tarekziade in [ #48484 ] Allow nested rope params for tiny models ( #48435 ) by @zucchini-nlp in [ #48435 ] Fix sliding-window mask layer_idx in Gemma3/Gemma4 create_masks_for_vision_model ( #48482 ) by @jiqing-feng in [ #48482 ] add xpu expectations for hunyuan_vl model tests ( #48504 ) by @kaixuanliu in [ #48504 ] [ Qwen 3.5 Moe ] Fix decorators ( #48436 ) by @vasqu in [ #48436 ] Infinite loop in dependency search ( #48393 ) by @zucchini-nlp in [ #48393 ] [serge] Fix 2 integration tests for model fsmt failing with output_mismatch (tensor values differ (2)) ( #48496 ) by @sergereview[bot] in [ #48496 ] Fix some tests by removing the deprecation cycle ( #48503 ) by @Cyrilvallez in [ #48503 ] Remove deprecation ( #48500 ) by @Cyrilvallez in [ #48500 ] Fix Pix2StructTextAttention init using hidden_size instead of d_kv ( #47558 ) by @ in [ #47558 ] Fix pre patch release utility ( #48499 ) by @Cyrilvallez in [ #48499 ] Update dev version ( #48498 ) by @Cyrilvallez in [ #48498 ] Fix Inkling inputs_embeds and add more tests ( #47827 ) by @Cyrilvallez in [ #47827 ] Simplify and fix qwen4 tests ( #48340 ) by @Cyrilvallez in [ #48340 ] [docs] Partial checkpointing and group_by_length ( #48463 ) by @stevhliu in [ #48463 ] doc: fix syntax error and typos in VibeVoice documentation ( #48489 ) by @VimalN2005 in [ #48489 ] [ Qwen4 Exp ] Use partial to avoid skipping mask more easily ( #48456 ) by @vasqu in [ #48456 ] Add support for NeuCodec ( #47143 ) by @harryjulian in [ #47143 ] [serge] Fix 1 integration tests regressed by commit bd95093 (PR #47493 ) ( #48426 ) by @sergereview[bot] in [ #48426 ] Fix rotary embedding regression ( #48477 ) by @Cyrilvallez in [ #48477 ] Remove deprecated mask functions ( #48476 ) by @Cyrilvallez in [ #48476 ] [MTP] Save memory by only capturing the last layer's hidden_states ( #48475 ) by @Cyrilvallez in [ #48475 ] Allow capturing only necessary hidden_states with capture_outputs ( #48081 ) by @sywangyi in [ #48081 ] Support per-layer MTP configuration ( #48264 ) by @eladsegal in [ #48264 ] [docs] Fix code snippets ( #47772 ) by @stevhliu in [ #47772 ] [Fix] Sparse TikToken tokenizers silently fail ( #48446 ) by @remi-or in [ #48446 ] No inherit decorator for NeoMME ( #48457 ) by @zucchini-nlp in [ #48457 ] Batch Rebalance Data Sampler ( #47340 ) by @delock in [ #47340 ] [serge] Fix 2 integration tests for model cwm failing with import_or_config (other (2)) ( #48414 ) by @sergereview[bot] in [ #48414 ] fix: Add DEIMv2 attribution ( #48448 ) by @harshaljanjani in [ #48448 ] [fix] inkling: mps + cuda mel spec extraction ( #47432 ) by @eustlb in [ #47432 ] fix: decode() batch path respects self.clean_up_tokenization_spaces ( #47793 ) by @lorenzozanee in [ #47793 ] Grounding dino fp16 dtype [backlog] ( #48438 ) by @molbap in [ #48438 ] Init the process group with a load-scaled timeout for sharded loading ( #48228 ) by @qgallouedec in [ #48228 ] Raise a clear error when a token is both forced and suppressed ( #47511 ) by @qgallouedec in [ #47511 ] Clarify device placement in pipelines ( #47367 ) by @LysandreJik in [ #47367 ] Add offload to gradient checkpointing ( #48444 ) by @qgallouedec in [ #48444 ] [MiniCPMV4_6] Update test_small_model_vision_generation_batch expected output (value drift) ( #48406 ) by @ydshieh in [ #48406 ] [serge] Fix 2 integration tests for model hyperclovax failing with other (other (2)) ( #48440 ) by @sergereview[bot] in [ #48440 ] fix(models): Drop the position-indexed token type lookup in RoPE encoders ( #48407 ) by @harshaljanjani in [ #48407 ] Document image_hidden_states/pixel_values mutual exclusivity for SmolVLM/Idefics2/Idefics3 ( #47714 ) by @verma8076 in [ #47714 ] Re-order a bit for easier navigation ( #48434 ) by @zucchini-nlp in [ #48434 ] Deprecated stuff gone ( #48367 ) by @zucchini-nlp in [ #48367 ] [serge] Fix 6 integration tests for model seamless_m4t_v2 failing with other (other (6)) ( #48425 ) by @sergereview[bot] in [ #48425 ] [Docs]: Update GLM 5.3 ( #48401 ) by @Dovis01 in [ #48401 ] Avoid print to stdout that fails the job check_failed_tests job ( #48391 ) by @ydshieh in [ #48391 ] Fix incorrect tuple return annotations on forward methods returning a Tensor ( #48359 ) by @Gronoxx in [ #48359 ] Fix interval merge invariant in _find_disjoint ( #47860 ) by @sharmax-vikas in [ #47860 ] skip mtp slow tests for now ( #48328 ) ( #48329 ) by @tarekziade in [ #48329 ] Update Tailscale action version in workflow ( #48394 ) by @glegendre01 in [ #48394 ] fix some failure in xpu ( #48252 ) by @sywangyi in [ #48252 ] [Improvement] Make gated delta rule more explicit ( #47625 ) by @remi-or in [ #47625 ] [CB] Fix wrong device scoping ( #48370 ) by @remi-or in [ #48370 ] Bump transformers-mlinter to 0.1.5 and clear the new findings ( #48259 ) by @tarekziade in [ #48259 ] fix: flash-attn fallback failing on torch2.13 ( #48388 ) by @NanoCode012 in [ #48388 ] [LongcatFlash] Fix test_longcat_generation_cpu: use device_map="cpu" to avoid MoE disk offload issue ( #48377 ) by @ydshieh in [ #48377 ] [Qwen3VLMoe] Update test_small_model_integration_test_batch expected output (value drift) ( #48376 ) by @ydshieh in [ #48376 ] [ONNX] Skip affected models on torch 2.13 (two dynamo regressions) ( #48191 ) by @ydshieh in [ #48191 ] Fix safe_open mmap memory exhaustion on Windows by using pread backend ( #48341 ) by @eryk-roch in [ #48341 ] Fix Zamba2 construction for num_mem_blocks > 1 checkpoints ( #48325 ) by @john-rocky in [ #48325 ] [Docs] Change 5.3 Flash pos in toc ( #48366 ) by @Dovis01 in [ #48366 ] [conftest] Use get_cpu_ram_total_gib for psutil patch (cgroup-aware) ( #48290 ) by @ydshieh in [ #48290 ] Fix flaky test_training_gradient_checkpointing for BigBirdPegasus (fp noise filter) ( #48332 ) by @ydshieh in [ #48332 ] Quiet continuous batching at default verbosity ( #48314 ) by @qgallouedec in [ #48314 ] Wait for the first request in the async continuous batching bootstrap ( #48304 ) by @qgallouedec in [ #48304 ] Ignore a stale best checkpoint recorded in a resumed trainer state ( #48319 ) by @VaggelisGian in [ #48319 ] [docs] Fix links and remove TokenizerFast ( #47748 ) by @stevhliu in [ #47748 ] Fix incorrect token classification prefix for ESMC ( #48348 ) by @Rocketknight1 in [ #48348 ] Create the continuous batching CPU group with local synchronization ( #48302 ) by @qgallouedec in [ #48302 ] Resolve continuous batching config against the text config for composite models ( #48299 ) by @qgallouedec in [ #48299 ] [ CI ] Unblock fast CI for now (failing tests) ( #48344 ) by @vasqu in [ #48344 ] [qwen4_exp] disable torch/onnx export tests due to data-dependent control flow ( #48345 ) by @ydshieh in [ #48345 ] [debug] Trace previous CI run selection in get_previous_daily_ci.py ( #48338 ) by @ydshieh in [ #48338 ] Normalize HunYuanVL's legacy field aliases via attribute_map ( #48261 ) by @hmellor in [ #48261 ] Significant community contributions The following contributors have made significant changes to the library over the last release: @ydshieh [fix] Update stale expected strings in HunYuanVL integration tests ( #48646 ) [fix] Update stale golden values and fix expected_logits shape in FlavaForPreTraining integration tests ( #48639 ) [tests] Fix integration test golden values broken by fast image processor default (PR #41388 ) ( #48637 ) [KimiLinear] Fix test_cpu_offload: set num_local_experts=4 in model tester ( #48624 ) Fix GPU memory teardown in CLI serve tests ( #48618 ) [fix] Fix how we read package versions - triggered by torch 2.14+ ( #48615 ) [Docker] Upgrade CPU torch to <=2.14.0, torchcodec to <=0.16.0 ( #48614 ) Add PR comment CI for AMD (MI300) ( #48065 ) [MiniCPMV4_6] Update test_small_model_vision_generation_batch expected output (value drift) ( #48406 ) Avoid print to stdout that fails the job check_failed_tests job ( #48391 ) [VibeVoice] Skip generate export tests (flaky) ( #48396 ) [LongcatFlash] Fix test_longcat_generation_cpu: use device_map="cpu" to avoid MoE disk offload issue ( #48377 ) [Qwen3VLMoe] Update test_small_model_integration_test_batch expected output (value drift) ( #48376 ) [ONNX] Skip affected models on torch 2.13 (two dynamo regressions) ( #48191 ) Retry get_daily_ci_runs on stale GitHub API cache ( #48374 ) [conftest] Use get_cpu_ram_total_gib for psutil patch (cgroup-aware) ( #48290 ) Fix flaky test_training_gradient_checkpointing for BigBirdPegasus (fp noise filter) ( #48332 ) [qwen4_exp] disable torch/onnx export tests due to data-dependent control flow ( #48345 ) [debug] Trace previous CI run selection in get_previous_daily_ci.py ( #48338 ) @LauraGPT Add Fun-ASR-Nano model ( #46180 ) @tarekziade Fix AttributeError in gradient_checkpointing_enable(offload=True) ( #48590 ) Compress the agent conventions file and document two modular pitfalls ( #48586 ) CI: Point test fixtures at hf-internal-testing copies we already host ( #48521 ) Retire test_multi_gpu_data_parallel_forward ( #48508 ) QA: Add noisy comment checker ( #48484 ) skip mtp slow tests for now ( #48328 ) ( #48329 ) Bump transformers-mlinter to 0.1.5 and clear the new findings ( #48259 ) @remi-or [Fix] Fix A10 expectations for a test ( #48454 ) Kimi linear ( #48250 ) [Fix] Use dedicated helpers for DeepGEMM and SonicMoE tests ( #48523 ) [Fix] Sparse TikToken tokenizers silently fail ( #48446 ) [Improvement] Make gated delta rule more explicit ( #47625 ) [CB] Fix wrong device scoping ( #48370 ) [CB] Fail faster ( #48334 ) @ArthurZucker Add h4 ( #48473 ) @harryjulian Add support for NeuCodec ( #47143 ) @delock Batch Rebalance Data Sampler ( #47340 ) @harshaljanjani fix: Add DEIMv2 attribution ( #48448 ) model: Add NVIDIA Canary-1B-v2 to Transformers ( #46825 ) fix(generation): Enforce the auto-compile cache check for encoder-decoder models ( #48364 ) fix(models): Drop the position-indexed token type lookup in RoPE encoders ( #48407 ) @tonywu71 Add NeoMME and NeoMME-Retriever ( #47992 ) @Dovis01 [Docs]: Update GLM 5.3 ( #48401 ) [Docs] Change 5.3 Flash pos in toc ( #48366 ) [Glm 5.3 Flash] GLM 5.3 Flash Support ( #48342 ) @pengzhiliang Implement VibeVoice ( #40546 )

rss:dc241cf0e1f293b8Useful?👍👎

86%opinionreddit-releases

My side project made $130 in its first month. A few thoughts

/u/don_gustavo

Usable today 86%Measured result 61%

Abstract

It’s been a month since I launched my website, and honestly I’m surprised it made any money at all. It runs on affiliate links, and almost all of the traffic comes from Reddit. Link: https://shapefinder.cc/ Some background: I’ve been a UI/UX designer for 8 years. AI lets me build small projects that I never could have pulled off on my own before. The idea came from my own frustration and from questions my friends kept asking me. I’m pretty deep into PC peripherals, so I get “is this mouse/keyboard any good?” a lot. One evening I decided to build a really simple tool to help with that. I posted it on Reddit, and to my surprise people really liked it. I’m not going to pretend it’s some massive success, but it makes me really happy that people actually get something out of it. I built the whole thing in Claude Code, and it’s been constant iteration: tweaking the UI, reworking the matching algorithm, adding features. Feedback from actual users helped me more than anything. A lot of what’s on the site now exists because someone in the comments said “it’d be cool if...” or pointed out something that didn’t make sense. If I had one piece of advice for anyone building their own project, it’d be this: try to think outside the box when it comes to how you present information. If you look at my site, it’s basically just a peripherals aggregator. I could have made it a plain list with filters on the side. What makes it stand out is how it presents things. It feels more personal, and you can actually see things change and adapt to what you need as you go. For me, the biggest thing AI gives me is the ability to explore. I’ll often just ask it to “show me this in 20 different ways.” Most of them are meh, but a few are usually interesting. I pick the best ones and keep iterating on those. Before, trying out that many directions would’ve taken me days. Now it’s an evening. UX still matters a lot. LLMs tend to build everything within the same narrow set of patterns. If you just take the first thing they give you, your project will look like everyone else’s, and that’s exactly where you can stand out. submitted by /u/don_gustavo to r/ClaudeAI [link] [comments]

rss:8f358b43b38963f8Useful?👍👎

86%releasereleases-llama-cpp

llama.cpp b11716

github-actions[bot]

Usable today 86%First-party 83%

Abstract

hexagon: add support for Q1_0 and Q2_0 ( #30122 ) hexagon: add support for q1_0 quant hexagon: enable Q1_0 weights in the 2D HMX MUL_MAT path hex-q1: cleanup gates, generalize tiling and remove in-loop divs hex-tiling: document tiled formats in developer.md and update htp-tensor.h hex-q2: add support for Q2_0 hex-q2: remove stale asserts and speacial cases hex-mm: fix MUL_MAT_NX assumption about square tiling, Q1/Q2 have non-square tiles hex-q1/2: minor optimizations (unrolls/un-unrolls/...) hex-q1: optimize Q1_0 layout to reduce unpack/vrmpy overhead hex-q1/q2_0: further optimize vec_dots Co-authored-by: Max Krasnyansky maxk@qti.qualcomm.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/57016509 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

rss:7d74938d9f0b9cf0Useful?👍👎

86%announcementhuggingface-blog

Welcome PaliGemma 2 – New vision language models by Google

First-party 86%Usable today 54%

rss:0bbf70132e4c6223Useful?👍👎

85%analysisreddit-releases

Accenture just capped Claude spend at $29/person after some staff hit $1,000 months. Most companies are wasting the same money.

/u/Panic_Lion

Measured result 85%

Abstract

This week Accenture told staff the company will only centrally fund about $29 a month per person for Claude. Some people had been running $500–$1,000 months. That is what a "successful" rollout looks like when nobody knows what Claude is for: seats get used for slides, emails, and summaries, the bill climbs, finance slams a cap, and the people who were actually doing real work get cut with everyone else. Anthropic already said why this happens. In their Economic Index last year they wrote that deploying AI for complex work "might be constrained more by access to information than on underlying model capabilities." In my experience after doing 100+ Claude Rollouts, there's 5 key things to make sure you are making the most out of Claude when dealing with a company rollout: Stop reporting weekly active users. Report whether a handoff stopped happening by hand. One shared Project per recurring job — not one Project called "Finance." Finish connector setup Turn how your best people work into Skills. Talk to five people who do the work, not the process owner, before you write the Skill. Only then give Cowork a multi-step job with a clear "done." If you own the Claude bill at your company: what % of spend is chats vs a shared Skill that actually owns a step? Article here submitted by /u/Panic_Lion to r/ClaudeAI [link] [comments]

rss:0cbbe75fbbd4e29dUseful?👍👎

85%releasebluesky49 likes · 2 reposts · 1 reply

Tool: GeoJSON Map Viewer

Simon Willison · @simonwillison.net, simonwillison.net · discussion

Usable today 85%First-party 44%

Abstract

In another case of it being quicker to build a tool than hunt around to find something that does what you need, here's a little vibe-coded thing for turning one or more GeoJSON shapes into a rendered PNG simonwillison.net/2026/Sep/1/g... Linked page: View and visualize GeoJSON data on an interactive OpenStreetMap with customizable styling options. Paste GeoJSON objects (Feature, FeatureCollection, or Geometry) into the editor, adjust fill color an...

bsky:f86cee298401e8aaUseful?👍👎

85%releasereleases-transformers

Release: v5.16.0

Cyrilvallez

Usable today 85%First-party 81%

Abstract

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. It mixes multiple residual streams with fine-grained elementwise gating before each attention and Mixture-of-Experts (MoE) block, then controls how much of the block output is injected back into each stream. QSA uses multiple query heads to score compressed key blocks, selects the most relevant contiguous token blocks, and keeps the incomplete trailing block uncompressed. This block-level selection reduces indexing overhead and improves memory locality for long sequences. Combined with Gated DeltaNet, QSA makes Qwen4-Exp the first hybrid architecture to integrate linear and sparse attention, substantially improving inference efficiency for long-context workloads. PLE enriches selected decoder layers with layer-specific lexical features derived from hashed token n-grams and a dilated depthwise convolution. Links: Documentation Add Qwen4Exp model ( #48337 ) by @Cyrilvallez in #48337 GraniteSpeech5 Granite Speech 5.0 Turbo CTC is a lightweight (~470M parameters) conformer encoder for automatic speech recognition, trained with Connectionist Temporal Classification (CTC) on BPE targets. It is a fast, encoder-only member of the Granite Speech family: transcription requires a single forward pass followed by greedy CTC decoding, with no autoregressive decoder. Architecturally, it extends the Granite Speech conformer CTC encoder with: Frame stacking + block-wise time subsampling : the feature extractor stacks pairs of log-mel(+delta) frames (2x), and the first two conformer blocks each subsample time by 2 through a stride-2 depthwise convolution (with a mean-pooled residual), for a total 8x time reduction at 10 ms mel hop. Block attention with Shaw's relative positional embeddings : attention is computed over fixed-size blocks (the sequence is right-padded to a whole number of blocks, with padded frames masked out), using separate bias-free query/key/value projections. Self-conditioned CTC : the CTC posteriors of the middle layer are projected and fed back into the hidden states, and the CTC head is shared between this mid-layer self-conditioning and the final prediction. Links: Documentation Add Granite Speech 5.0 - ( #48288 ) by @eustlb in #48288 Step3p7 Step-3.7-Flash was proposed in Step 3.7 Flash by StepFun. It is a 198B-parameter sparse Mixture-of-Experts vision-language model, pairing a 196B-parameter MoE language backbone with a 1.8B-parameter vision encoder for native image understanding. StepFun hasn't published a technical report for Step-3.7-Flash, so the details below are drawn from the released checkpoint's configuration rather than a paper. Sparse MoE decoder : all but the first 3 decoder layers route through a MoE block of 288 routed experts (top-8 per token) plus a single shared expert. The router scores experts with a sigmoid and a learned per-expert bias instead of an auxiliary load-balancing loss, the same strategy as DeepSeek-V3 . Gated attention : each attention layer adds an extra projection whose sigmoid output gates the attention output per head, before the output projection — the same Gated Attention mechanism used in Qwen3-Next . A subset of layers use fewer heads and a sliding window instead of full attention. Multi-token prediction : some checkpoints ship extra decoder layers trained for multi-token prediction, which [ ~GenerationMixin.generate ] can use for speculative decoding via use_mtp=True . Vision encoder : a SigLIP-style ViT with 2-D rotary position embeddings and a learned per-layer scale on the attention and MLP branches. Its output is downsampled 4x by two stride-2 convolutions before a linear projector maps it into the text model's hidden size. Dynamic image tiling : instead of a fixed tile grid, the image processor picks its tiling window from each image's own aspect ratio, producing one downscaled global view plus zero or more local high-resolution crops per image. Links: Documentation [new model] step 3.7 ( #46658 ) by @itazap in #46658 CohereCompass CohereCompass is the base architecture for small, specialized (vision-)language models trained by Cohere. Links: Documentation Add CohereCompass modeling ( #47878 ) by @calpt in #47878 ESMC and ESMFold2 ESMC and ESMFold2 are new state-of-the-art protein language and folding models from BioHub. ESMC is trained with a masked language modeling objective, and it can be easily transferred to sequence and token classification tasks for proteins. Checkpoints exist in various sizes, from 300M parameters up to 6B parameters. It works as a drop-in replacement for older ESM-2 and ESM-3 models, with significantly higher accuracy. ESMFold2 is a state-of-the-art protein folding model which produces high accuracy predictions. It uses an iterated diffusion approach that is significantly different from the original ESMFold, offering huge improvements in accuracy for more complex structures. Links: Documentation ESMC , Documentation ESMFold2 Port ESMC and ESMFold2 to Transformers ( #46419 ) by @Rocketknight1 in #46419 Breaking changes The legacy tensor-parallel implementation has been replaced with a DTensor-native backend, so users relying on the previous TP API for inference or training must migrate to the new DTensor-based interface. 🚨 TP dtensor API inference + training ( #47579 ) by @3outeille attn_implementation="sdpa" dispatch is now properly supported for wav2vec2_conformer, wav2vec2-bert, and SeamlessM4T/v2 models, which may change initialization behavior for users who previously worked around this limitation. 🚨[wav2vec2] Support attn_implementation=sdpa dispatch ( #46196 ) by @YangKai0616 FuyuProcessor no longer returns the image_patch_indices output, so any code that depends on this field must be updated to remove references to it. 🚨 Leftover processors ( #47924 ) by @zucchini-nlp Cache Several cache-related bugs were fixed in this release, including an off-by-one error in the sliding window cache, Whisper speculative decoding cache corruption, CpmAnt use-cache failures, Qwen2.5-Omni/Qwen3-Omni-MoE generation with compilable caches, and compressed-tensors loading for KV-cache-only quantized models. Documentation was also added for cache token removal using negative values, and per-layer cache configuration support (allowing models to use different cache settings per layer) was introduced. Cpmant fix use cache ( #48013 ) by @jiqing-feng in [ #48013 ] [docs] Cache crop ( #47950 ) by @stevhliu in [ #47950 ] Revert "Support per-layer cache configuration and attention-mask selection" ( #48175 ) by @Cyrilvallez in [ #48175 ] Support per-layer cache configuration and attention-mask selection ( #47901 ) by @eladsegal in [ #47901 ] Fix Qwen2.5-Omni / Qwen3-Omni-MoE generation with a compilable cache ( #47872 ) by @jiqing-feng in [ #47872 ] Fix sliding window cache index off-by-one on wraparound ( #47708 ) by @hameedibrh in [ #47708 ] [Whisper] Fix speculative decoding: UnboundLocalError, cache corruption, and speed regression ( #48000 ) by @ydshieh in [ #48000 ] Fix compressed-tensors loading for KV-cache-only quantized models ( #47904 ) by @kylesayrs in [ #47904 ] Generation This release fixes several generation bugs across multiple models, including Whisper speculative decoding issues (UnboundLocalError, cache corruption, speed regression, and left-padded batch position IDs), broken image generation in Emu3, garbage output in OLMo/GPTNeoX, and Qwen2.5-Omni/Qwen3-Omni-MoE generation with compilable caches. Additionally, logit distributions for candidate generators using sampling are now aligned by returning logits after applying logit processors. [Whisper] Fix speculative decoding: preserve cleared suppress tokens through super().generate() ( #48108 ) by @ydshieh in [ #48108 ] [Whisper] Fix decoder position IDs for left-padded batches in longform generation ( #48028 ) by @ydshieh in [ #48028 ] [serge] Fix 2 integration tests for model generation failing with import_or_config (other (2)) ( #48061 ) by @sergereview[bot] in [ #48061 ] Align logit distributions for CandidateGenerators using sampling ( #48007 ) by @Cyrilvallez in [ #48007 ] [GPTNeoX] Fix post_processor not overridden when loading from pretrained (OLMo garbage generation) ( #47988 ) by @ydshieh in [ #47988 ] [emu3] 🦮 Black Labrador is back! Fix image generation broken since #37033 ( #47948 ) by @ydshieh in [ #47948 ] Attention Several attention-related bug fixes were made in this release, including correcting a SigLIP2 documentation typo, fixing Flash/SDPA attention dispatch tests for xcodec2 and ROCm RDNA GPUs, resolving a GPT2 cross-attention mask being silently discarded, and enabling SDPA support declaration in TimmWrapper . Per-layer cache configuration and attention-mask selection support was also introduced, allowing models with heterogeneous layer configurations to use distinct sliding_window , attention_chunk_size , and number_of_conv_states values per layer. doc: Fix typo in SigLIP2 Flash Attention code example ( #48197 ) by @VimalN2005 in [ #48197 ] [xcodec2] Fix flex attention and flash dispatch tests ( #48244 ) by @jiqing-feng in [ #48244 ] Fix ROCm SDPA-flash skip guard that crashes on RDNA GPUs ( #47965 ) by @Abdennacer-Badaoui in [ #47965 ] [GPT2] Fix encoder_attention_mask being silently discarded in cross-attention ( #47946 ) by @DavidJohnQuinlan in [ #47946 ] Declare sdpa support in TimmWrapper ( #47939 ) by @jiqing-feng in [ #47939 ] Quantization Quantization improvements include adding NVFP4 quantization support via HF kernels (enabling on-the-fly BF16 weight quantization with ~50% memory reduction), and fixing several bugs: reverting a regression in is_quantization_compressed that caused incorrect module layouts for packed-format checkpoints, fixing CLIP weight initialization failures with quantized checkpoints, and restoring KV-cache quantization setup for KV-cache-only quantized models. Revert "[Quantization]: Refactor is_quantization_compressed for format-based detection" ( #48072 ) by @subin9 in [ #48072 ] feat: add nvfp4 quantization ( #47883 ) by @drbh in [ #47883 ] [DeepSeekV2] Fix integration tests OOM: use device_map=auto instead of 8-bit quantization ( #47991 ) by @ydshieh in [ #47991 ] Fix CLIP _init_weights when a child module carries quantized weights ( #47921 ) by @Bluear7878 in [ #47921 ] Parallelization Introduced a naive pipeline parallel inference engine supporting tied/untied weight embeddings with seamless generate() integration, while restoring backward compatibility for the tensor-parallel API with a deprecation cycle for tp_plan in from_pretrained() . Additionally fixed a model parallel bug in the BLT model affecting beam search. Restore BC for the tensor-parallel API ( #48300 ) by @ArthurZucker in [ #48300 ] fix bug for blt model parallel bug ( #48327 ) by @kaixuanliu in [ #48327 ] Pipeline parallel naive inference ( #47289 ) by @3outeille in [ #47289 ] Kernels Kernel support was improved with documentation updates highlighting supported models, a fix for export crashes on kernel-decorated functions by adding a is_torchdynamo_exporting guard, and the default Flash Attention 2 hub kernel version was bumped to v3 to resolve compatibility issues with newer PyTorch versions. [docs] Kernel supported models ( #48258 ) by @stevhliu in [ #48258 ] [Fix] Export crashes on kernel-decorated function ( #47808 ) by @remi-or in [ #47808 ] Bump default flash-attn2 hub kernel version to v3 ( #47863 ) by @jiqing-feng in [ #47863 ] Bugfixes and improvements Fix video-llama modular conversion ( #48336 ) by @zucchini-nlp in [ #48336 ] CI: gate the hunyuan-moe slow test ( #48330 ) by @tarekziade in [ #48330 ] Add a regression test for force_accelerate_hooks signature preservation ( #48260 ) by @wtdcode in [ #48260 ] Add an opt-in per-frame pixel cap (cap_pixels_per_frame) to the Qwen3-VL video processor ( #48071 ) by @dkrisman in [ #48071 ] Fix scores type in stopping criteria docstrings ( #47676 ) by @qgallouedec in [ #47676 ] Docstring check didn't match some file - fix it ( #48121 ) by @zucchini-nlp in [ #48121 ] Add shared ImageProcessingTester ( #47745 ) by @guarin in [ #47745 ] Fix AutoTokenizer returning TokenizersBackend for DeepSeek-R1-Distill-Qwen models ( #48211 ) by @ydshieh in [ #48211 ] [docs] Fix failing doctests ( #47687 ) by @stevhliu in [ #47687 ] Fix build_2d_sinusoidal_position_embedding on MPS ( #47897 ) by @guarin in [ #47897 ] [Gemma4] Investigate flaky test_generation_beyond_sliding_window_1_eager ( #48236 ) by @ydshieh in [ #48236 ] Let gradient checkpointing skip layers with every_n_layers ( #48200 ) by @qgallouedec in [ #48200 ] Disable daily nightly CI ( #48292 ) by @remi-or in [ #48292 ] gs ( #48288 ) by @eustlb in [ #48288 ] CI: fix muse OOMs ( #48284 ) by @tarekziade in [ #48284 ] Fix BayesianDetectorModel.from_pretrained() by calling post_init() ( #48254 ) by @woojinpaik in [ #48254 ] [Fix] Avoid duplicating tests in CI ( #48287 ) by @remi-or in [ #48287 ] [ GDN ] Fix recurrent FLA fallback ( #48266 ) by @vasqu in [ #48266 ] Fix tie_word_embeddings not lifted from text_config for some VLM configs (BC regression) ( #45857 ) by @qgallouedec in [ #45857 ] replace xpu-smi subprocess call in benchmark_v2 ( #48083 ) by @kaixuanliu in [ #48083 ] ignore mlinter ci file ( #48267 ) by @tarekziade in [ #48267 ] Fix dtype mismatch in grouped_mm_fallback for LoRA training on Mamba+… ( #47933 ) by @adh-aakriti in [ #47933 ] Compute MoE load-balancing loss per layer to avoid giant one-hot materialization −99.7% @ 128k ( #48131 ) by @qgallouedec in [ #48131 ] deterministic layer_types buffer registration in multiple models ( #48162 ) by @mowoe in [ #48162 ] Fix nemotron_h save_pretrained emitting singular backbone.embedding.weight ( #48075 ) by @yuekaizhang in [ #48075 ] force_accelerate_hooks should not hide the signature it wraps ( #48156 ) by @SunMarc in [ #48156 ] fix(data_collator): align TokenClassification numpy_call with torch_call ( #48212 ) by @ in [ #48212 ] Fix a typo in a use of a local variable field_ in a test ( #48184 ) by @AleksMat in [ #48184 ] [serge] Fix 2 integration tests regressed by commit 16780c8 (PR #47622 ) ( #48134 ) by @sergereview[bot] in [ #48134 ] [Gemma4] Fix stale expected values in integration tests ( #48233 ) by @ydshieh in [ #48233 ] [TableTransformer, PI0] Fix stale expected values and OOM in integration tests ( #48198 ) by @ydshieh in [ #48198 ] Post two CI badges on a PR: CPU PR CI and GPU run-slow ( #48190 ) by @tarekziade in [ #48190 ] Fix stale expected values in integration tests (cuda sm_86 / Aug04 regressions) ( #48171 ) by @ydshieh in [ #48171 ] Assign a reviewer even when a codeowner has left, and route models by modality ( #48085 ) by @tarekziade in [ #48085 ] [VITS] Un-skip test_model_forward ( #46375 ) by @blipbyte in [ #46375 ] Apply context parallelism to the evaluation path ( #48167 ) by @qgallouedec in [ #48167 ] Fix gpt_oss runs on GPU ( #48118 ) by @tarekziade in [ #48118 ] [EsmFold2] Fix stale expected distogram logit values ( #48182 ) by @ydshieh in [ #48182 ] Fix Apr 05 integration test regressions (cuda sm_86) ( #48170 ) by @ydshieh in [ #48170 ] Add MLU support to is_flash_linear_attention_available ( #46995 ) by @atri2549 in [ #46995 ] Fix integration test expected values for cuda sm_86 (Mar 15 regressions) ( #48168 ) by @ydshieh in [ #48168 ] Fix DynamicCache reconstruction during ExecuTorch export ( #47900 ) by @eladsegal in [ #47900 ] [LLaVA] Fix pixtral integration tests for cuda sm_86 ( #48166 ) by @ydshieh in [ #48166 ] Port ESMC and ESMFold2 to Transformers ( #46419 ) by @Rocketknight1 in [ #46419 ] [Qwen2.5-Omni] Update stale expected values for cuda sm_86 ( #48164 ) by @ydshieh in [ #48164 ] [Mistral3] Fix batched integration tests: padding_side=left + update expected values ( #48161 ) by @ydshieh in [ #48161 ] Fix DeepSeek V2 default vocab size ( #48159 ) by @hmellor in [ #48159 ] [InternVL] Fix stale expected values for Llama integration tests (cuda sm_80) ( #48153 ) by @ydshieh in [ #48153 ] Retry transient network errors (RemoteDisconnected) in github_utils ( #48124 ) by @ydshieh in [ #48124 ] fix bugs for clvp model ( #47127 ) by @kaixuanliu in [ #47127 ] Always tie embeddings for LongT5 and Pop2Piano ( #47620 ) by @jiqing-feng in [ #47620 ] Use generator with seed for LengthGroupedSampler in Trainer._get_eval_sampler for deterministic eval order with per_device_eval_batch_size > 1 ( #48025 ) by @philipshurpik in [ #48025 ] Enable mlinter findings artifact for inline PR reviews ( #48117 ) by @ydshieh in [ #48117 ] Fix CpmAnt loading: size lm_head to vocab_size ( #48012 ) by @jiqing-feng in [ #48012 ] Delete old mlinter review comments before posting new ones ( #48107 ) by @ydshieh in [ #48107 ] [Video] Warn and return all frames when num_frames exceeds total_num_frames ( #48074 ) by @carlszk in [ #48074 ] Accept artifact dir as argument in post_mlinter_review.py ( #48106 ) by @ydshieh in [ #48106 ] Fix mlinter artifact path ( #48088 ) by @ydshieh in [ #48088 ] [Video] Fix convert_to_rgb channel slicing and alpha blending for RGBA videos ( #48053 ) by @ in [ #48053 ] [PE] Skip test_sdpa_can_dispatch_on_flash for TimmWrapper-backed models ( #48064 ) by @ydshieh in [ #48064 ] fix(pipeline): preserve model.generation_config precedence over pipeline defaults ( #47752 ) ( #47953 ) by @nithin42 in [ #47953 ] [Gemma3] Update integration test expected values for A10G ( #48036 ) by @ydshieh in [ #48036 ] Fallback from 'lanczos' to 'bicubic' when on cuda ( #48026 ) by @zucchini-nlp in [ #48026 ] [serge] Fix 2 integration tests regressed by commit b9090ae (PR #47096 ) ( #48060 ) by @sergereview[bot] in [ #48060 ] [Fix] Small FA-related test failures in CB ( #47341 ) by @remi-or in [ #47341 ] fix: honor empty processor_kwargs={} in multimodal pipelines ( #48044 ) by @ in [ #48044 ] [CircleCI] Enable CI for private forks, no-op for public repo ( #48056 ) by @ydshieh in [ #48056 ] Support BatchFeature in length-grouped samplers ( #48034 ) by @qgallouedec in [ #48034 ] Fix EOS for candidate generators ( #47931 ) by @ in [ #47931 ] [Gemma3n] Update integration test expected values for A10G + torch 2.13 ( #48035 ) by @ydshieh in [ #48035 ] fix failed test cases for muse_glimmer ( #48011 ) by @kaixuanliu in [ #48011 ] Cohere compass tests ( #47895 ) by @zucchini-nlp in [ #47895 ] docs: use relative paths for README language menus and add fa/ro entries ( #47777 ) by @Priyans-Lathiya in [ #47777 ] Moving mlinter to 0.1.4 ( #47918 ) by @tarekziade in [ #47918 ] [MoE] Fix Blackwell GPU crash with torch._grouped_mm on torch <= 2.8 ( #48014 ) by @ in [ #48014 ] [docs] Muse Glimmer ( #47882 ) by @stevhliu in [ #47882 ] [Florence2] Fix two integration test failures caused by torch 2.13 and auto-dtype ( #48031 ) by @ydshieh in [ #48031 ] Proper separation of tests ( #47943 ) by @zucchini-nlp in [ #47943 ] unpin pytest in the examples_torch deps ( #48023 ) by @tarekziade in [ #48023 ] [CI] Fix startup failure in pr_build_doc_with_comment workflow by adding missing get-pr-number dependency ( #47971 ) by @ in [ #47971 ] Let the GPU verify caller turn on the memory probe ( #48001 ) by @tarekziade in [ #48001 ] Fix MTP config when mlp_layer_types is absent ( #48015 ) by @Cyrilvallez in [ #48015 ] Remove duplicate block_sparse_moe assignment in GraniteMoeDecoderLayer ( #47876 ) by @Aman2394 in [ #47876 ] [ModernVBERT] Fix integration test checkpoint (404 since April) ( #48009 ) by @ydshieh in [ #48009 ] Fix cropping ( #48006 ) by @Cyrilvallez in [ #48006 ] Fix DFlash candidate token device mismatch with device_map="auto" ( #47877 ) by @sywangyi in [ #47877 ] 🔴 Allow tokenizers 0.23.1 ( #46381 ) by @ArthurZucker in [ #46381 ] [Whisper] Fix batch decode_with_timestamps in WhisperTokenizer.decode() ( #47997 ) by @ydshieh in [ #47997 ] [OLMoE] Update expected logits for A10G and add torch.no_grad() ( #47989 ) by @ydshieh in [ #47989 ] [OLMo] Fix OOM in logits tests by adding torch.no_grad() ( #47986 ) by @ydshieh in [ #47986 ] [AXK1] Fix expected logits for CUDA A10G ( #47980 ) by @ydshieh in [ #47980 ] [Gemma] Update expected values for A10G ( #47976 ) by @ydshieh in [ #47976 ] Fix gemma4 video to device ( #47896 ) by @guarin in [ #47896 ] Remove stale (None, None) fallback in qwen2_5_vl batch_different_resolutions test ( #47972 ) by @ydshieh in [ #47972 ] Potential fix for code scanning alert no. 267: Artifact poisoning ( #47949 ) by @tarekziade in [ #47949 ] Fix GatedDeltaNet A_log dtype to prevent -inf under bfloat16 init ( #47944 ) by @Nkluge-correa in [ #47944 ] [serge] Fix 2 integration tests for model got_ocr2 failing with other (other (2)) ( #47937 ) by @sergereview[bot] in [ #47937 ] Fix Jinja block endings in CHAT WITH MODELS' Writing a chat template … ( #47960 ) by @ak1for2business-prog in [ #47960 ] [tests] Fix expected output for Qwen2.5-VL batch_wo_image on CUDA ( #47968 ) by @ydshieh in [ #47968 ] [serge] Fix 2 integration tests for model opt failing with other (other (2)) ( #47909 ) by @sergereview[bot] in [ #47909 ] Scan a diff in trufflehog, not the whole repo history ( #47945 ) by @tarekziade in [ #47945 ] [serge] Fix 2 integration tests for model vivit failing with output_mismatch (tensor values differ (2)) ( #47566 ) by @sergereview[bot] in [ #47566 ] Fix Gemma sliding_window being halved on every config save/reload ( #47940 ) by @Bluear7878 in [ #47940 ] docs: fix incorrect PEFT anchor link in fine-tuning section ( #47927 ) by @dsulot in [ #47927 ] CI: add vllm-test-init and vllm-test-transformers jobs on dedicated runners ( #47934 ) by @ydshieh in [ #47934 ] Make muse glimmer exportable ( #47871 ) by @IlyasMoutawwakil in [ #47871 ] docs: add installation instructions for NVIDIA Spark (ARM64) devices ( #47906 ) by @mfuntowicz in [ #47906 ] [CohereCompass] Minor docs fixes ( #47903 ) by @calpt in [ #47903 ] fix: correct checkpoints, config annotations, and create_dummy_models improvements ( #47902 ) by @ydshieh in [ #47902 ] Transform paths and repeat joining for response parsing ( #47648 ) by @Rocketknight1 in [ #47648 ] [docs] Update toctree ( #47781 ) by @stevhliu in [ #47781 ] Add CI_CPU_MEMORY_LIMIT_GB to check_failed_tests workflow ( #47884 ) by @ydshieh in [ #47884 ] Use tiny Hub checkpoint in Qwen3ASR processor test ( #47833 ) by @ydshieh in [ #47833 ] Update AutoRound XPU/CPU backend ( #47826 ) by @yiliu30 in [ #47826 ] docs(tests): fix typos in test comments ( #47859 ) by @zhaoxinyi02 in [ #47859 ] Update version post release ( #47870 ) by @Cyrilvallez in [ #47870 ] Significant community contributions The following contributors have made significant changes to the library over the last release: @tarekziade CI: gate the hunyuan-moe slow test ( #48330 ) CI: fix muse OOMs ( #48284 ) ignore mlinter ci file ( #48267 ) Post two CI badges on a PR: CPU PR CI and GPU run-slow ( #48190 ) Assign a reviewer even when a codeowner has left, and route models by modality ( #48085 ) Fix gpt_oss runs on GPU ( #48118 ) Moving mlinter to 0.1.4 ( #47918 ) unpin pytest in the examples_torch deps ( #48023 ) Let the GPU verify caller turn on the memory probe ( #48001 ) Potential fix for code scanning alert no. 267: Artifact poisoning ( #47949 ) Scan a diff in trufflehog, not the whole repo history ( #47945 ) @dkrisman Add an opt-in per-frame pixel cap (cap_pixels_per_frame) to the Qwen3-VL video processor ( #48071 ) @jiqing-feng Cpmant fix use cache ( #48013 ) [xcodec2] Fix flex attention and flash dispatch tests ( #48244 ) Always tie embeddings for LongT5 and Pop2Piano ( #47620 ) Fix CpmAnt loading: size lm_head to vocab_size ( #48012 ) Fix Qwen2.5-Omni / Qwen3-Omni-MoE generation with a compilable cache ( #47872 ) Bump default flash-attn2 hub kernel version to v3 ( #47863 ) Declare sdpa support in TimmWrapper ( #47939 ) @ydshieh Fix AutoTokenizer returning TokenizersBackend for DeepSeek-R1-Distill-Qwen models ( #48211 ) [Gemma4] Investigate flaky test_generation_beyond_sliding_window_1_eager ( #48236 ) [Gemma4] Fix stale expected values in integration tests ( #48233 ) [TableTransformer, PI0] Fix stale expected values and OOM in integration tests ( #48198 ) Fix stale expected values in integration tests (cuda sm_86 / Aug04 regressions) ( #48171 ) [EsmFold2] Fix stale expected distogram logit values ( #48182 ) Fix Apr 05 integration test regressions (cuda sm_86) ( #48170 ) Fix integration test expected values for cuda sm_86 (Mar 15 regressions) ( #48168 ) [LLaVA] Fix pixtral integration tests for cuda sm_86 ( #48166 ) [Qwen2.5-Omni] Update stale expected values for cuda sm_86 ( #48164 ) [Mistral3] Fix batched integration tests: padding_side=left + update expected values ( #48161 ) [InternVL] Fix stale expected values for Llama integration tests (cuda sm_80) ( #48153 ) Retry transient network errors (RemoteDisconnected) in github_utils ( #48124 ) [Whisper] Fix speculative decoding: preserve cleared suppress tokens through super().generate() ( #48108 ) Enable mlinter findings artifact for inline PR reviews ( #48117 ) Delete old mlinter review comments before posting new ones ( #48107 ) Accept artifact dir as argument in post_mlinter_review.py ( #48106 ) Fix mlinter artifact path ( #48088 ) [Whisper] Fix decoder position IDs for left-padded batches in longform generation ( #48028 ) [PE] Skip test_sdpa_can_dispatch_on_flash for TimmWrapper-backed models ( #48064 ) [Gemma3] Update integration test expected values for A10G ( #48036 ) [CircleCI] Enable CI for private forks, no-op for public repo ( #48056 ) [Gemma3n] Update integration test expected values for A10G + torch 2.13 ( #48035 ) [Florence2] Fix two integration test failures caused by torch 2.13 and auto-dtype ( #48031 ) [ModernVBERT] Fix integration test checkpoint (404 since April) ( #48009 ) [Whisper] Fix speculative decoding: UnboundLocalError, cache corruption, and speed regression ( #48000 ) [Whisper] Fix batch decode_with_timestamps in WhisperTokenizer.decode() ( #47997 ) [Whisper] Fix integration test failures on A10G (dtype, stale values, API changes) ( #47995 ) [DeepSeekV2] Fix integration tests OOM: use device_map=auto instead of 8-bit quantization ( #47991 ) [OLMoE] Update expected logits for A10G and add torch.no_grad() ( #47989 ) [OLMo] Fix OOM in logits tests by adding torch.no_grad() ( #47986 ) [GPTNeoX] Fix post_processor not overridden when loading from pretrained (OLMo garbage generation) ( #47988 ) [AXK1] Fix expected logits for CUDA A10G ( #47980 ) [Gemma] Update expected values for A10G ( #47976 ) [emu3] 🦮 Black Labrador is back! Fix image generation broken since #37033 ( #47948 ) Remove stale (None, None) fallback in qwen2_5_vl batch_different_resolutions test ( #47972 ) [tests] Fix expected output for Qwen2.5-VL batch_wo_image on CUDA ( #47968 ) CI: add vllm-test-init and vllm-test-transformers jobs on dedicated runners ( #47934 ) fix: correct checkpoints, config annotations, and create_dummy_models improvements ( #47902 ) Add CI_CPU_MEMORY_LIMIT_GB to check_failed_tests workflow ( #47884 ) Use tiny Hub checkpoint in Qwen3ASR processor test ( #47833 ) @eustlb gs ( #48288 ) @eladsegal Fix DynamicCache reconstruction during ExecuTorch export ( #47900 ) Support per-layer cache configuration and attention-mask selection ( #47901 ) @YangKai0616 🚨[wav2vec2] Support attn_implementation=sdpa dispatch ( #46196 ) @drbh feat: add nvfp4 quantization ( #47883 ) @Priyans-Lathiya docs: use relative paths for README language menus and add fa/ro entries ( #47777 ) @itazap [new model] step 3.7 ( #46658 ) @calpt [CohereCompass] Minor docs fixes ( #47903 ) Add CohereCompass modeling ( #47878 )

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85%announcementhuggingface-blog

Introducing the AMD 5th Gen EPYC™ CPU

First-party 85%Usable today 32%

rss:53d92ede468fb39bUseful?👍👎

84%analysisreddit-industry

I built a launch video generator SaaS and Generated over $1600+ in revenue.

/u/Humble-Original2934

Measured result 84%Usable today 67%First-party 45%

Abstract

In Feburary 2026, I launched a Launch Video Generator SaaS product called Clickcast as a CS Student with literally Zero budget. No investors. No paid marketing. No expensive infrastructure. I built it using a combination of free tiers, student credits and a lot of experimentation with my own Idea. And eventually, it crossed $1600+ in revenue till date. This is the exact story of how I built it, what I spent, how I got my first users, what worked, what failed, and what I would do differently if I were starting again today. 1. The idea Clickcast was an idea that originated from my own problem. I was creating many projects, and every time after completing one project, I wanted to post it on LinkedIn, X, etc. However, what I didn’t want to do was just put up some screenshots of my website. I wanted to make my launch more special. I was looking for a concise, interesting video that would tell the viewer about: \- My creation \- The problem that my creation solves \- How it works \- Why should the viewer care The challenge? Making that video would be a lot of effort on my part. I would need to learn video editing skills, work on animations, use software such as After Effects, make a recording of my product, edit the whole process, add transitions and text to the videos, and spend hours even days for creating one single video. Thus, I decided to look for another way out. I went online and started looking for tools that would create me an automatic explainer video from my project or website within a couple of minutes. I tried using some of the most popular tools such as InVideo, Canva, etc. However, I was unable to find what I needed. Mostly, I saw tools that were turning my projects into some kind of PowerPoint presentations in video form. I wasn’t looking for some generic video marketing tool. I was searching for a video marketing tool that would actually understand my website and represent it in the best way possible. >This is when the idea occurred to me: Why not create a website which will be creating explainer videos for any website? And that was how Clickcast came into existence. Concept was quite straightforward: Provide Clickcast with your website URL -> AI understands the product -> creates a video for you in minutes. Solution that I found to solve my own problem, ultimately turned out to be a working SaaS product. And here is the journey of how I made it without any initial capital and reached $1,600+ revenue till date. 2. Building the MVP Once the concept clicked, I did research on how to actually make it work. Firstly, I had to figure out how to convert a website URL to a video. In my research, I discovered two components which made the basis of Clickcast that are Playwright , Remotion and AI for writing Script. Playwright had the ability to open and automate the process of working with the website using just a URL. It allowed me to crawl through the website, get insight about it and take screenshots of the actual product. Secondly, Remotion. Remotion is a component that allows creating videos programmatically with React. In other words, instead of creating the video manually in After Effects, it allowed me to describe the video and its behavior using code. However, shortly after, I faced yet another challenge. I wanted every video to be unique. It would’ve been rather simple to implement if I used the traditional template approach. Website → Template → Text/Screenshots replacement → Video This, however, was not what I wanted. If 1,000 different websites used Clickcast, I didn’t want all 1,000 videos to look like the same template with different screenshots. A software development tool should have a totally different approach than the SaaS dashboard. A portfolio website should look differently than an e-commerce website. A productivity application should have a unique story from an AI tool. So I would need to find a way to create that video based on each website separately. And that’s when the idea came up: What if an AI could create the video code? Earlier I was just using AI to write script of the video , but then I decided to use it to write custom code too that will be converted to video using remotion. Instead of creating numerous templates, I would provide an AI model with information about the website and make it create the video code. >The process looked somewhat like this: Website URL → Playwright → Website information + Screenshots → AI → Video code → Remotion → Video For example, AI could choose: \- What the video should cover \- Which elements of the website should be highlighted \- How the narrative should develop \- What animations should be used \- What text should be included \- How transitions between different sections should look \- How the product should be featured Then, Remotion would use the code and create the actual video. Thus, at its most basic level, my first iteration of Clickcast consisted of the following three components: 1. Playwright Needed to access the website and retrieve the required information. 2. AI Needed to analyze the information obtained from the website and dynamically generate the video code. 3. Remotion Needed to render the dynamic video code and create the video. Such was the underlying concept of my MVP. Of course, it had its flaws. The AI would sometimes generate incorrect code, animations would not always go as planned, and it was far more difficult than I thought to get a consistently high-quality video from an arbitrary website. However, with this solution, I got my very first working prototype that could receive any website URL and generate a promotional video automatically. And with that, I had everything I needed to further develop Clickcast . 3. Building with almost $0 This was where there arose a rather obvious issue: How was I going to finance everything? As a second-year BTech Computer Science student, I did not have any budget to finance a SaaS application. However, being a student provided me with one massive benefit: Student credits. I got access to the GitHub Student Developer Pack that provided me with many valuable tools for Clickcast development. This included access to GitHub Copilot, free .tech domain name, and $100 Azure credits. On top of that, Google offered one year of Gemini Pro to verified students, thus providing me with access to Antigravity without having to spend money right away on using AI to write code. This is not all I did. I leveraged free trials and credits offered by such platforms as: \- Supabase \- Vercel \- Google Cloud $300 credits \- AWS $200 credits >Which changed my mindset from: I don’t have money to build a SaaS. To: How far can I take this with the resources available to me? And I was able to go pretty far. These credits helped me to pay for a big chunk of the infrastructure and development costs during Clickcast’s creation and development period. Obviously, free credits won’t transform your SaaS into something magically free to run. But as long as you have MVP or are just starting out, it becomes quite easy to ignore infrastructure and AI costs, because the user base is not that large yet and videos aren’t generated too often. But with student discounts it became possible to experiment, build, launch and iterate on my product in a professional way without raising funds and spending tons of money. I guess that was the biggest advantage of being a student at that point. I didn’t have much money. But I had access to many tools. 4. Getting the first users Now came the next problem. I had built the product. But how do I get people to use it? I remembered a term I had heard much earlier: #BuildInPublic The idea was simple instead of disappearing for months and only showing the finished product, I would share the journey while I was actually building it. So I started documenting Clickcast on LinkedIn. My first Clickcast post went live before Clickcast was even launched. At that point, I was still building the product. I didn’t even have a complete frontend. I had a very basic MVP, mostly focused on the backend. But I kept posting updates. Every week, I shared what I was building, what I had learned, what I was working on, and how Clickcast was progressing. And for a long time, LinkedIn was basically the only place where I was consistently documenting the journey. Then I built a waitlist. After about a month, I finally put together a simple landing page for Clickcast and opened a waitlist. It wasn’t even a fully functional product yet. The frontend wasn’t connected to the actual Clickcast backend. It was essentially a simple landing page with a Supabase-powered email collection system for people interested in trying the product. But it was enough to test whether anyone actually cared about the idea. At around the same time, I was also fairly active on Reddit. So I randomly decided to post about Clickcast there on reddit. And that post turned out to be a major turning point . I got 20+ people on the waitlist . Today, 20 people might not sound like a huge number. But at that stage, it meant something completely different to me. I was a student sitting alone and building a product that nobody had asked me to build. And suddenly, 20+ real people were waiting for something I had created. That was a huge motivation boost. So I accelerated development. About 10 days later , I launched Clickcast publicly on Reddit and LinkedIn. And then something happened that I wasn’t expecting. On the first day: 50+ people signed up. And more importantly: 50+ videos were generated. For a product that had started as a personal problem and a rough MVP, seeing actual people use it was an incredible feeling. But there was a problem. A pretty big one. My Free Product Wasn’t Actually Free . At launch, I had made Clickcast completely free . I was also pretty new to running a production SaaS at this scale. I hadn’t yet figured out how expensive video generation could become when real users started using the product. And then I looked at my Azure usage. My credits were disappearing FAST. In just around two days , Clickcast had burned through a huge amount of my Azure Student credits. I even posted about the situation on LinkedIn. So I started optimizing. I looked at the infrastructure, especially the worker responsible for processing video-generation jobs, and started figuring out how to scale it down when it wasn’t needed. After several iterations, I managed to reduce the infrastructure cost by roughly 90% . That was a huge lesson for me: Building the product is only half the job. Running it efficiently is another challenge altogether. A few days later, another important milestone happened. My Dodo Payments account was approved. Until then, Clickcast was completely free. Now I could finally put a payment system in place. So I added a payment wall and started experimenting with charging users for credits. And then came a moment I’ll probably always remember. I woke up one morning, saw notification that someone had purchased credits on Clickcast. $1. That’s it. Just one dollar. But it was my first Internet dollar . From there, I continued building in public experimenting with pricing, improving the product, optimizing infrastructure costs, talking to users, and figuring out what people actually wanted. And slowly, those experiments started turning a student project into a real SaaS business. submitted by /u/Humble-Original2934 to r/SaaS [link] [comments]

rss:d37a7cd8fcdbca73Useful?👍👎

84%releasereleases-llama-cpp

llama.cpp b11719

github-actions[bot]

Usable today 84%First-party 80%

Abstract

ggml-cuda: fattn do not check oob if k_VKQ_max = (k_VKQ_max - nbatch_fa) ( #30572 ) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/57114852 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

rss:45858dc90028bf1aUseful?👍👎

84%releasereleases-transformers

Release v5.20.1

vasqu

Usable today 84%First-party 82%

Abstract

Release v5.20.1 Special surprise including the new model Beam from Reflection AI 🥳 Beam is a token-efficient, frontier-scale model focused on coding and agentic use cases. It is built for teams that want to run a powerful model on their own hardware, infrastructure, or cloud, and customize it to their specific needs. Links: Documentation Fix small bug in auto mappings ( #49664 ) by @yonigozlan in #49664 Some real patch fixes We broke loading models on older torch versions 😢 should be fixed now. But here is the full list of fixes Fix HunYuan-VL image assignment during batched generation ( #49557 ) by @Arnav66692 Fix DeepGEMM Mega MoE under expert parallelism( #49428 ) by @IlyasMoutawwakil Fix from_pretrained on torch 2.7: require torch>=2.8 for distributed checkpointing ( #49652 ) by @naveecode Make DtensorShardOperation work on torch 2.6 and 2.7 ( #49657 ) by @hazelxue Full Changelog : v5.20.0...v5.20.1

rss:c891552bf683eadcUseful?👍👎

84%announcementhuggingface-blog

Llama can now see and run on your device - welcome Llama 3.2

First-party 84%Usable today 67%

rss:60be7740b2dcf927Useful?👍👎

83%measurementreddit-ai

AI is getting better at doing real science while becoming dramatically cheaper. GPT-6 Astra takes #1 in scientific research, beating Claude Sonnet 5.5 while using 22× fewer tokens. GPT-6.1 Sol also beats Sonnet at nearly 1/10th the cost. Scientific research is starting to scale with compute.

/u/141_1337

Measured result 83%

Abstract

submitted by /u/141_1337 to r/singularity [link] [comments]

rss:c877218e6a70101aUseful?👍👎

83%releasereddit-ai

AI and the Coop Everything Repo let me add ONLINE Coop to Zelda: Ocarina of Time! Fully synced boss battles, shared items, shared dungeons, synced cutscenes, time travel together, and join each other through steam!

/u/Salty_Molasses5801

Usable today 83%First-party 36%

Abstract

The other day I posted my co-op anything repo , that is much inspired by the reverse engineer anything repo, with a huge amount of knowledge and skills on how to make any game co-op. It also features as a community hub, so your agent can joint and submit its own PR for its own game, and help submit info and knowledge to further improve the repo, and make adding co-op to more games using AI agents more of a community effort! We also made a discord here if you want to join that. Some other games being worked on include: Deus Ex (released), Fallout NV, Enter the Gungeon, Gothc 3, Deep Rock Galactic: Survivor, God of War and more! My friend and I have dreamed of playing zelda ocarina of time together, but wanted it to be fully synced for the best possible co-op experience. It's now over the line :) Coded with Opus 5.5 orchestrating a TON of sonnet 5.5 agents, honestly a match made in heaven And if you want to play it! https://github.com/cranebeaker/ocarina-coop#readme submitted by /u/Salty_Molasses5801 to r/accelerate [link] [comments]

rss:f6e3f970f44ef811Useful?👍👎

83%releasehn

Open MicroManipulator – A sub-micrometer 3D motion control plattform

_Microft

Usable today 83%

Abstract

Article URL: https://github.com/0x23/MicroManipulatorStepper Comments URL: https://news.ycombinator.com/item?id=50140001 Points: 189 # Comments: 14

rss:e23c37be7611428cUseful?👍👎

83%releasereleases-llama-cpp

llama.cpp b11721

github-actions[bot]

First-party 83%Usable today 80%

Abstract

cuda : fuse qwen4exp's SCALE -> SIGMOID -> SCALE -> hc_post chain ( #30602 ) Port of the Vulkan HC_POST_GATE fusion ( #29520 ). hc_combine builds the hc_post weights as 2 sigmoid(inject/hc); dsv4_hc_post_f32 now takes a gated template flag and computes scale_out sigmoid(scale_in*p) from the raw weights, so the chain runs as one kernel instead of four. Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/57168730 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

rss:da44befa6412ba07Useful?👍👎

83%releasereleases-llama-cpp

llama.cpp b11717

github-actions[bot]

First-party 83%Usable today 83%

Abstract

hexagon: add support for hadamard MUL_MAT and GET_ROW for flat and tiled Q1_0 and Q2_0 ( #30627 ) hex-hadamard: add support for hadamard ops hex-mm: reduce pkt counts in the q8_1 quantizer and vec_dot_q1 hex-q1/q2: add support for flat and tiled GET_ROWS for Q1_0 and Q2_0 Co-authored-by: Max Krasnyansky maxk@qti.qualcomm.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/57059545 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

rss:89437831b9d4fadcUseful?👍👎

83%releasesimonwillison

llm-tools-datasette 0.1.1

Usable today 83%First-party 74%

Abstract

Release: llm-tools-datasette 0.1.1 When I switched LLM over to httpx2 in LLM 0.33 I forget that this would break any plugins which used httpx without directly depending on it. llm-tools-datasette was one of those plugins, now fixed. Tags: datasette , llm

rss:6084701491891644Useful?👍👎

82%releasereleases-llama-cpp

llama.cpp b11718

github-actions[bot]

Usable today 82%First-party 80%

Abstract

Restore ESIMD kernels on FFN path ( #30239 ) Reenable ESIMD on the FFN path Signed-off-by: Todd Malsbary todd.malsbary@intel.com Extend ESIMD fusion on FFN path to include Q8_0 Signed-off-by: Todd Malsbary todd.malsbary@intel.com Remove comment explicitly referencing ESIMD in test-backend-ops Signed-off-by: Todd Malsbary todd.malsbary@intel.com Signed-off-by: Todd Malsbary todd.malsbary@intel.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/57072547 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

rss:25d19154fc6f679cUseful?👍👎

82%releasereleases-llama-cpp

llama.cpp b11715

github-actions[bot]

Usable today 82%First-party 71%

Abstract

ggml-cuda: HIP: use __builtin_amdgcn_perm for q1_0 unpacking ( #29927 ) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/57002849 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

rss:028d5d55f8b498b9Useful?👍👎

82%announcementhuggingface-blog

Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI

First-party 82%Usable today 70%

rss:1c10baab0defe0beUseful?👍👎

82%announcementhuggingface-blog

Training mRNA Language Models Across 25 Species for $165

Measured result 82%Usable today 46%First-party 36%

rss:89890b6841f6e990Useful?👍👎

82%releasehuggingface-blog

NVIDIA Releases 6 Million Multi-Lingual Reasoning Dataset

First-party 82%Usable today 69%

rss:4391fb476962c465Useful?👍👎

81%releasereleases-llama-cpp

llama.cpp b11724

github-actions[bot]

First-party 81%Usable today 81%

Abstract

CUDA: fix shared-memory WAR hazard in flash_attn_ext_vec ( #30573 ) CUDA: fix KQ write-after-read race in flash_attn_ext_vec Assisted-by: ChatGPT CUDA: apply review suggestions for KQ sync comments Co-authored-by: Johannes Gäßler johannesg@5d6.de Co-authored-by: Johannes Gäßler johannesg@5d6.de Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/57270560 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

rss:f7003d9b0e584beeUseful?👍👎

81%releasereleases-llama-cpp

llama.cpp b11720

github-actions[bot]

First-party 81%Usable today 78%

Abstract

ggml: support non-contiguous SUM (CPU, CUDA, Vulkan, WebGPU) ( #28851 ) ggml: support non-contiguous SUM (CPU, CUDA) tests: add non-contiguous SUM cases tests: SUM over non-contiguous views vulkan: support non-contiguous SUM webgpu: support non-contiguous SUM Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/57164866 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (CUDA 12) - CUDA 12.8 libraries Ubuntu x64 (CUDA 13) - CUDA 13.4 libraries Ubuntu arm64 (CUDA 13) - CUDA 13.4 libraries Ubuntu x64 (ROCm 10.0) Ubuntu x64 (OpenVINO) Ubuntu x64 (SYCL FP32) Ubuntu x64 (SYCL FP16) Linux arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Android: Android arm64 (CPU) Android arm64 (Snapdragon: CPU, Adreno GPU, Hexagon NPU) - setup guide Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows arm64 (OpenCL Adreno) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.4 DLLs Windows arm64 (CUDA 13) - CUDA 13.4 DLLs Windows x64 (Vulkan) Windows arm64 (Vulkan) Windows x64 (OpenVINO) Windows x64 (SYCL) Windows x64 (ROCm 10.0) openEuler: DISABLED openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph) UI: UI

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81%announcementinterconnects

Introducing our Artifacts Hub and Adoption Dashboard

Nathan Lambert

First-party 81%Usable today 37%

Abstract

Scaling our curation and measurement of the open ecosystem.

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81%measurementhuggingface-blog

Up to 3.2x Faster Inference with LFM2.5-DSpark

Measured result 81%First-party 61%Usable today 34%

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81%announcementhuggingface-blog

huggingface_hub v1.0: Five Years of Building the Foundation of Open Machine Learning

First-party 81%Usable today 47%

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81%announcementhuggingface-blog

Introducing Trackio: A Lightweight Experiment Tracking Library from Hugging Face

First-party 81%Usable today 61%

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81%announcementhuggingface-blog

We now support VLMs in smolagents!

First-party 81%Usable today 56%

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80%announcementhuggingface-blog

Introducing Storage Buckets on the Hugging Face Hub

First-party 80%Usable today 37%

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80%announcementhuggingface-blog

NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets

First-party 80%Usable today 45%

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80%releasehuggingface-blog

Llama 3.1 - 405B, 70B & 8B with multilinguality and long context

First-party 80%Usable today 59%

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Near misses (10): just under your maybe bar, useful for tuning thresholds

Filtered by your exclusions (124)

Built with Paper Radar v0.1.0. Percentages are the model's probability that a paper matches your interest statement, not a quality score. Every decision is logged in data/decisions/. Label a paper with paper-radar label <id> yes|no, then run paper-radar calibrate.