2026-10-16

While behind the ECI frontier, Argon is the closest Google’s come since Gemini 3.1 Pro in Feb, and a big jump from Gemini 3.8 Flash (157). Source: Epoch.ai submitted by /u/Marimo188 to r/singularity [link] [comments]

Black Forest Labs (BFL), the frontier AI research lab building visual intelligence, today announced the open weights release of FLUX 3 Image Klein, its most capable open weights image model to date. FLUX 3 Image Klein provides the quality of a large frontier model with the customization and speed of open weights, runs on consumer GPUs and laptops, and retains features from the larger FLUX 3 Image model such as native 4K generation, bounding boxes, and pixel perfect editing. The release includes a base model for fine-tuning, a distilled model for low-latency generation, and quantized FP8, NVFP4, and INT8 versions. Following September’s open weights release of FLUX 3 Action for robotics, today’s release brings the company’s latest research to core creative use cases, giving developers and businesses greater freedom to build, customize, and deploy visual AI. Making its research and technology available for others to build on is one of Black Forest Labs’ founding principles. BFL has consistently released its research findings alongside open weight versions of its industry-leading FLUX models, enabling others to build on its advances. So far in 2026, the team has published its Self-Flow research and released open weights for the FLUX.2 [klein] family in January and FLUX 3 Action in September. Self-Flow improved generation across images, video, and audio, while helping models learn representations that can also support robot control. This research led to BFL’s most capable model to date, FLUX 3, which brings multimodal generation and action prediction into a shared architecture. Today’s release opens access to the creative capabilities of the same FLUX 3 backbone, with further open weights releases from the FLUX 3 family to follow. https://huggingface.co/collections/black-forest-labs/flux-3-image-klein submitted by /u/hyxon4 to r/StableDiffusion [link] [comments]

Hey r/LocalLlama - Today we’re excited to release Muse Spark Open, the most capable US open-weight model to date. Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning. Key Model Specs Parameters: 816B total / 42B active Context Window: 1M tokens Modalities: Text, image, video, audio Safety Post-Training: Additional safety post-training, with evaluations conducted under Meta's Superintelligence Scaling Framework. Inference, fine-tuning & deployment Helion GPU kernels: Ships with custom, auto-tunable Helion kernels in SGLang, providing 1.03–3.95× kernel speedups across NVIDIA and AMD GPUs and a 3% E2E improvement on AMD MI350. Engines: Runs on leading inference engines including SGLang, vLLM, TensorRT-LLM, Hugging Face Transformers, Apple MLX, llama.cpp and Unsloth, with optimizations for AMD, Apple, Intel and NVIDIA silicon. Optimized for Apple Silicon: To help make advanced models more accessible, Muse Spark Open is optimized to run on two Apple M5 Ultra Mac Studios with the latest MLX framework. Fine-tuning: Developers can further customize the model with PyTorch, including TorchTitan for fine-tuning. API platforms: Available today through Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter, Together AI. Where to download: Model weights are available for download on Amazon SageMaker AI, Google Cloud Model Garden, Hugging Face, and Microsoft Foundry Models. We’re releasing the weights under the Muse Model License, with limited commercial terms that let many developers use it for free. We look forward to your feedback! Learn more on our research blog: https://go.meta.me/museopen Download the weights: https://huggingface.co/collections/meta-models/muse-spark-open submitted by /u/AIatMeta to r/LocalLLaMA [link] [comments]

Models on HF: https://huggingface.co/collections/prism-ml/bonsai-2-small WebGPU demo: https://huggingface.co/spaces/webml-community/ternary-bonsai-2-hermes-webgpu submitted by /u/xenovatech to r/LocalLLaMA [link] [comments]

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

To all those who are bemoaning missing getting DGX Sparks for under $4000. Here's your chance. The brand new just announced RTX Spark laptop is on sale at HP. Configured with 128GB of RAM, it comes out to $3832.49. Use coupon anniversary25 to take 25% off of the $5109.99 regular price. https://www.hp.com/us-en/shop/pdp/hp-omnibook-x-laptop-next-gen-ai-14-ks000-14-cb1l6av-1 submitted by /u/fallingdowndizzyvr to r/LocalLLaMA [link] [comments]

Yesterday Fish released this incredible text-to-speech model, so naturally I spent the rest of the day building a great demo for them :D The model is a chunky MoE (35B3A), but it runs well on a DGX Spark. The 4bit quants get very close to the original. You can try it here: https://huggingface.co/spaces/fishaudio/Drama-3 or download the model and run it locally 🤗 submitted by /u/futterneid to r/LocalLLaMA [link] [comments]

Overview llama.cpp v0.7.0 adds the LiliCorr speculative decoding technique and MTP for GLM5-Next, a new GPU cache for MoE experts kept in host memory ( --moe-cache-mib , up to ~2.2x decode speedup when experts are offloaded), and a large batch of new models: K2 Horizon (dense and MoVA), Prism Bonsai 2 27B, EmbeddingGemma-2 (text+vision+audio), the LiquidAI d1/d1-omni and vLLM decision 2/d3 decision models, pplx-decider, MiniCPM-V 4.7, Nemotron Super 3.5 VL / Nemotron Bolt and maion-coder. The server gets a refactored chat session API ( common_chat_session , part-based formatted output), much better slot and prompt-cache restore behavior, decision-model support for the new architectures and a new default port (9931), the web UI gains a full models manager, and ggml is updated to v0.27.0. Highlights New GPU cache for host-memory MoE experts ( --moe-cache-mib ): experts run on the GPU from an LRU cache, up to 2.2x faster decode #29887 The expert cache is now shared across multiple GPUs #30112 LiliCorr speculative decoding: candidate selection with a small draft transformer on top of DFlash #30506 GLM5-Next: MTP speculative decoding with an optimized sparse attention path #29928 K2 Horizon support in both dense and MoVA variants: conversion, tokenizer, chat template, -sm tensor splitting #29535 Prism Bonsai 2 27B ternary model support #29600 EmbeddingGemma-2: text + vision + audio embedding model #30054 New decision models: LiquidAI d1-3B, d1-omni-600M, vLLM decision 2, vLLM d3 and pplx-decider #30110 #30114 #30378 #30383 #30044 PLaMo-3 tokenizer implements reference pre-segmentation for exact tokenization parity #30045 RPC server supports -sm tensor (tensor-parallel RPC, RPC protocol v8) #26610 Server now listens on port 9931 by default instead of 8080 #30159 API changes include/llama.h : new moe_cache_size context param for the host-expert GPU cache #29887 include/llama.h : new LLAMA_VOCAB_TYPE_PLAMO3 vocab type #30045 include/llama.h : llama_perf_context_data reports graph reallocations during inference #30329 include/llama.h : embeddings now skips the LM head when a pooling type other than NONE is used #30445 include/llama.h : llama_batch_ext_set_embd_state returns false when the context has no state embedding, M-RoPE order documented #30506 tools/server : chat layer refactored around common_chat_session with feed() / finish() #30210 tools/server : formatted chat output is now an array of parts instead of a single string #30389 tools/server : default listening port changed to 9931 (Docker images keep 8080 inside the container) #30159 New models K2 Horizon: dense and MoVA variants with conversion, tokenizer, chat template and -sm tensor support #29535 Prism Bonsai 2 27B (ternary) #29600 EmbeddingGemma-2: text + vision + audio embeddings #30054 LiquidAI d1-3B and d1-omni-600M decision models #30110 #30114 vLLM decision 2 and vLLM d3 models #30378 #30383 pplx-decider decision model #30044 MiniCPM-V 4.7 multimodal model #29416 Nemotron Super 3.5 VL and Nemotron Bolt 74B A9B #30520 maion-coder architecture #29778 Mellum: MoE gating parameters read from GGUF and unified RoPE handling #30578 LiliCorr draft model conversion and graph #30506 Rerankers: classifier_activation support, exact GELU for ModernBERT encoders #29692 #30108 Core changes Speculative decoding: MTP for GLM5-Next, LiliCorr drafts, correct mtmd handling in MTP graphs #29928 #30506 #30257 Speculative decoding: shared nextn tensor flags with consolidated row cropping and sched re-reserve on flag change #30097 #30017 #30020 Speculative decoding: fixed draft sampling and rejection handling, DFlash output head sharing #30095 #30111 New --spec-draft-n-ubatch to size the draft context batch separately and save VRAM #30459 MoE expert cache: host-memory experts served from a device LRU cache, supported over multiple GPUs #29887 #30112 Embedding models: skip the logits buffer and the LM head when pooling is enabled (up to 10x less VRAM) #30255 #30445 Embedding graphs: reordered get_rows , dropped the redundant t_embd expand and leftover ggml_cont calls #30160 #30461 #30198 No-cache attention: padded KV dimension and KQ bias, avoided graph reallocations #30416 #30446 #30439 k-pool models: selection built in the causal mask layout, fixed a scatter data race on shared sequences #30480 #29994 GLM5-Next sparse attention: removed the gather path #30042 KV cache: honor LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY so draft checkpoints stop carrying the full KV #30514 Prefetching: merge ranges before issuing system prefetch calls, never map foreign ranges into a backend buffer #30532 #30405 Sampling: greedy selection for eligible temperature-zero chains, backend sampling graph kept static across ubatches #29797 #30223 Tokenizers: PLaMo-3 pre-segmentation and FiM tokens, opt-in NFC normalization for Unigram, strict UTF-8 validation #30045 #30090 #30390 #30485 Jinja: fixed loop variable scoping, Python-compatible tojson rendering, faster context copy #30533 #30483 #30362 #30562 Model split: distribute layers evenly when no device reports free memory #29677 Multi-modality changes Added mtmd_build_inp_attn_mask for building non-causal multimodal attention masks #30259 Added cohere2 vision encoder support #30062 MTP drafts now handle mtmd inputs, Qwen3.8 Flash Next accepts image embeddings #30257 #30397 New multimodal architectures: MiniCPM-V 4.7, Nemotron Super 3.5 VL, EmbeddingGemma-2 (vision + audio) #29416 #30520 #30054 Server changes /v1/systemone extended to the new decision models: d1-3B, d1-omni, vLLM decision 2/d3, pplx-decider #30110 #30114 #30378 #30383 #30044 Chat refactor: session-based parsing and part-based formatted output #30210 #30389 Generated text and tokens are now accumulated as parser input, preparing token-aware parsing #29876 Prompt cache: only restorable slot states are saved, so rerank and embedding requests no longer fill the cache #30498 Prompt cache: child tasks prefer empty slots and preserve the state they would overwrite #30567 A pinned busy slot is left untouched until the request can run on it #30295 Slot save/restore now keeps context checkpoints and the draft model state #26004 #30574 Fixed an off-by-two in the SWA reuse threshold that discarded restored states #30580 Fixed a stack-overflow crash when escaping special tokens with std::regex #30243 LLAMA_ARG_SLOT_SAVE_PATH environment variable for --slot-save-path #30272 New per-component server tests; vendored cpp-httplib updated to 0.61.0 #30312 #30494 #30134 #30081 UI changes New Models Manager: one table of all served models with expandable quants, search, filters, sorting and favorites #29583 Row actions: load/unload, delete from disk, hide/unhide, resumable downloads with progress #29583 Model configuration side drawer with model info, chat template and "start a new chat" / "use in this chat" actions #29583 Models manager follow-ups: GGUF training context, favorite UX, filter and sort fixes #30228 Fixed ui_settings not being applied on the first visit in router mode #29668 Fixed text attachment preview overflow for large pasted text #29898 Fixed the default agentic turns limit falling back to 10 instead of 100 #30582 ggml changes ggml updated to v0.27.0 : the backend scheduler gains user callbacks for split weight copying and compute-buffer reallocation, and the RPC protocol is bumped to v8 for tensor-parallel RPC servers. Backends gained chunked gated-delta-net kernels on CUDA/HIP, KleidiAI SME2 k-quant matmuls and AVX2 Q2_0 dot products on CPU, L2-aware coopmat2 scheduling and bf16 activations on Vulkan, reordered-layout MoE kernels (MXFP4, IQ3, Q5_K, grouped XMX GEMM) on SYCL, dp4a MoE GEMMs on OpenCL, tensor-API and wider-head-dim flash attention on Metal, and a DMA/HVX copy overhaul with pooling and 3D im2col ops on Hexagon. Correctness work focused on non-contiguous and strided views, grid and workgroup size limits, out-of-bounds accesses and well-defined mul_mat_id behavior for duplicate expert ids; symbol visibility now defaults to hidden Assets Nightly build: b11669 More info Releases and versioning of ggml-org projects Changelog since v0.6.0 620d222 llama.cpp : bump version to 0.7.0 ( #30603 ) cd0a5b6 models : add Nemotron Super 3.5 VL and Nemotron Bolt 74B A9B support ( #30520 ) a11b653 scripts : add ci-status.sh to list active CI runs ( #30586 ) 61ad80b server: fix the off-by-two in the SWA reuse threshold ( #30580 ) a6b91d8 model : Mellum - read MoE gating params from GGUF, unify RoPE ( #30578 ) ea736c1 server: save the draft state in the slot file ( #30574 ) b0d2391 server: prefer empty slots and keep states overwritten by child tasks ( #30567 ) c0cb53a sync : ggml 4ed1c85 ggml : bump version to 0.27.0 711780f ci : bump ty to 0.0.88 ( #30587 ) fd55cb9 fix(ui): take the agentic turns default from DEFAULT_AGENTIC_CONFIG ( #30582 ) 3e4dfa8 models : add maion-coder ( #29778 ) 08698c8 jinja : improve context copy constructor performance ( #30562 ) 82e1e2f jinja : write nan and inf in tojson as json.dumps does ( #30483 ) 47f5a64 vulkan : fix concurrent first device init ( #29766 ) e36df60 sycl : replace oneAPI package by DLE+oneDNN package ( #30131 ) 9166cea opencl: fix OOB tail access for adreno noshuffle gemv kernels ( #30421 ) 2130ca8 jinja : fix loop body variable scoping ( #30533 ) 23f83e0 ggml-cpu : optimize pad_f32 ( #30526 ) 2ce20d2 unicode : reject overlong UTF-8 and out-of-range codepoints ( #30485 ) 68ac765 llama : split llama_prefetch() into merge and prefetch steps ( #30532 ) 77c1f39 opencl: apply flash attn softcap before mask ( #30546 ) 733f57a vulkan : copy strided f16 KV once on AMD so reads spread across memory channels ( #27703 ) 5a638ae cpu: add F16 SWIGLU_OAI reference implementation and validation ( #29200 ) 6e66cb5 metal : add BF16 variant of the tensor API flash attention kernel ( #30491 ) e467e5b kv-cache: honor PARTIAL_ONLY in the plain KV cache ( #30514 ) 1ddc262 graph: keep the graph non empty without embeddings ( #30522 ) 2639a3b server: save only restorable slot states to the prompt cache ( #30498 ) 843e7dc spec : add lilicorr support ( #30506 ) 3e390af vulkan: Use L2-friendly workgroup scheduling for cm2 mulmat ( #30215 ) a0e6dc7 opencl: support CLAMP on non-contiguous views ( #30186 ) 23deec0 devops: incorrect COPY instruction ( #30529 ) 683bbc0 gguf-py : detect nfc normalization ( #30540 ) 529ac89 sycl: IQ3 code reorder ( #29107 ) 685f708 ggml-cpu : keep the FP16->FP32 lookup table on x86 with F16C ( #30342 ) ( #30343 ) c114bd4 docs: update IBM zDNN backend docs ( #30527 ) 5cdadfc ggml-cpu : size set_rows conversion buffer for all threads ( #30454 ) 62df181 cuda, hip: GDN chunked kernel ( #29353 ) 37d6d65 skill: Update code-review skill for UI development ( #30509 ) 6868a52 ggml: fix conv_1d result layout for batched input ( #28738 ) 3fb4767 common : add --spec-draft-n-ubatch ( #30459 ) 318a471 docs : clarify installation recommendation for Intel oneAPI Toolkit ( #30407 ) 8c2134a ggml : add note about mul_mat_id + disable mul_mat_id_dup tests ( #30501 ) df18bee model: split layers evenly when no device reports free memory ( #29677 ) 3eb1013 vendor : update cpp-httplib to 0.61.0 ( #30494 ) af0579c ggml : improve RPC tensor transfer logs ( #30481 ) 62bacfa cuda : use SIMT implicit GEMM conv2d on AMD without WMMA ( #30325 ) 77390ba jinja : write floats in tojson as Python does ( #30362 ) c5a9976 opencl: use buf+offset instead of subbuffer for adreno moe expert sort ( #30418 ) 21a19cd opencl: fix q8_0 dp4a mul_mat_id and mark PREC_F32 mul_mat_id unsupported for adreno ( #30365 ) 9cf967a models: drop the t_embd expand of the embedding graphs ( #30461 ) 4dd6dd4 HIP: use 64-row MMQ tiles for some quant types on RDNA3.5 ( #28195 ) 1431019 vulkan: fix compile error ( #30495 ) e7268bf llama : build the k-pool selection in the causal mask layout ( #30480 ) cdce056 vulkan: Add workaround for device free memory > total memory on Intel Windows ( #29835 ) d524bb6 vulkan: Define the shader count for Intel Arc Pro B65 + B70 ( #30431 ) 7ec420b vulkan: support mulmat bf16 activations ( #29069 ) 10da8c4 unicode : add opt-in NFC normalization for Unigram tokenizers ( #30390 ) eca5458 convert : export YaRN truncation config ( #30288 ) 091b6a7 Enable OpenSSL in FP32/FP16 SYCL builds to restore HF HTTPS downloads ( #30330 ) fcac190 webgpu: (Fix CI) fix mul_mat_id with duplicate expert ids ( #30451 ) 04f7226 server : escape the special tokens without std::regex ( #30243 ) ff026f8 chat: replace output string with parts ( #30389 ) 1b70350 ggml : simplify backend search path error handling ( #28367 ) d71e324 docs : update zDNN supported ops ( #30472 ) 45451c3 kleidiai: add SME2 Q4_K and Q6_K matmul support ( #29219 ) 77c4540 vulkan: Optimize pool_2d when the pooling window is large ( #30119 ) ecdd29e meta: recreate all subgraphs when the arena is reallocated ( #30453 ) ec0bfac hexagon: add configurable HTP DCVS/HMX power mode ( #30423 ) ace20f6 graph: pad the KQ bias of the no-cache attention ( #30446 ) b9f3599 llama : do not map foreign ranges into a backend buffer ( #30405 ) f441d2d model: load YaRN beta metadata for laguna models ( #30331 ) cb55ffe graph: skip the LM head for pooled embeddings ( #30445 ) a17e94d tests : consolidate context state tests into test-llama-context ( #30023 ) b476d9a llama : fix log message ( #30452 ) e0bcacc vulkan: allow multiple devices to access gpu-visible host memory ( #29741 ) 0a731cc ggml-cpu : add AVX2 dot product for Q2_0 ( #30447 ) f851e24 cuda : add GGML_CUDA_GRAPH_DEBUG and read env vars once ( #30327 ) bfe65e1 cuda : fix compiler warnings in mmq-load-tiles.cuh ( #30443 ) 411d548 vulkan : split matmul dispatch when it would exceed maxComputeWorkGroupCount ( #29533 ) 296538a spec : remove tokens assert from dsv4 graph_mtp ( #30444 ) 7f51953 model : allow Qwen3.8 Flash Next to accept image embds for MTP ( #30397 ) 882d671 cuda : fix warnings from dead breaks ( #30441 ) 30f7df1 cuda : warnings cleanup ( #30420 ) 9d7d504 convert : fix LoRA conversion with --outtype auto ( #30433 ) ed019cd llama : skip logits buffer allocation when graph produces no logits ( #30255 ) 9880157 graph : avoid reallocations in the "no cache" path ( #30439 ) af50f2f ggml-zdnn : introduce Q8_0 mat mul ( #29836 ) 071b553 models : remove redundant ggml_cont calls now supported by all unary kernels ( #30198 ) 4413a19 hexagon: extend scalar DMA broadcasting support for binary ops ( #30245 ) 8f574b0 ci : build OpenVINO releases with GGML_NATIVE=OFF and CPU backend variants ( #30324 ) a49f58d ci: fix OpenVINO download links ( #30401 ) cbe28bb model: fix vllm d3 multi seq case ( #30408 ) 358a420 graph: pad the KV dimension of the no-cache attention to 256 ( #30416 ) 48e9857 metal: fix metal tensor kernel_mul_mm error for large tensor ( #28748 ) 8bb3cf0 hexagon: optimize CPY/CONCAT — round 3, short-rows edition ( #30341 ) 0f701c8 vulkan: pack FMA for f16 accumulation, no dot product op ( #29877 ) efe3626 model: support vllm d3 models ( #30383 ) 097cf33 model: support vllm decision 2 model ( #30378 ) ccdbe29 tests : link each test against only the libraries it uses ( #30299 ) 073b740 metal : fix mul_mat_id with duplicate ids ( #30373 ) 77757a3 examples : fix draft sampling and rejection handling ( #30095 ) 972b5fc llama : report compute graph reallocations during inference ( #30329 ) 2f160ba ui: fix text attachment preview overflow ( #29898 ) f9a95ae sycl: guard DMMV reads at row tails ( #29338 ) 08ffba2 vulkan: use ternary expressions to avoid switch lowering for CM2 quant block size ( #30164 ) a05e58c ggml: Change default visibility of symbols to hidden ( #30014 ) b2f036c vulkan: fix buffer logging on 32-bit platforms after source split ( #30326 ) e937b17 hexagon: optimize q2_k bpw and activation ( #30035 ) b13a93a hexagon: add IM2COL_3D ( #30305 ) f2cc728 opencl: improve fa, allow dk512 for gemma-4, improve dk64 ( #30266 ) c83f305 opencl: fix image limit for q4_k dense bin kernels, q4_0 bcast and rms_norm ( #30310 ) 5c8854f rpc : turn GGML_RPC_DEBUG into a verbosity level and add logs ( #29544 ) a9d27ac model : support for Prism Bonsai 2 27B ( #29600 ) 1cf7767 cpu: fix AVX2 CPUs not using F16C when available ( #30270 ) 966baae vulkan: handle mul_mat_id duplicates in the prepass rather than looping ( #29998 ) 62a6f74 server: add component tests ( #30312 ) 23b0202 server: leave a busy slot untouched when a request pins it ( #30295 ) 69f201a model : support MiniCPM-V 4.7 ( #29416 ) abee0c8 cmake(s390x): disable z17 target for unsupported compilers ( #30297 ) aa94f20 args: add LLAMA_ARG_SLOT_SAVE_PATH env for --slot-save-path ( #30272 ) 781dbc5 spec: properly handle mtmd input for mtp ( #30257 ) 0fd868c mtmd: add build_inp_attn_mask ( #30259 ) 1623d8c cuda: always use MMVQ for MUL_MAT_ID on sm_60 ( #27828 ) 1bb2b9f CUDA/HIP: fix race in flash_attn_ext_f16_process_tile when nbatch_combine != DKQ/2 ( #30103 ) 2bbca8f ggml-cpu: vectorize fp32 to fp16 conversion ( #30157 ) 404f557 vulkan: use 4 rows for NVIDIA MUL_MAT_ID MMVQ ( #29274 ) b797c82 ggml: fix s390x all cpu build ( #30140 ) f2918ca opencl: add bin kernels kernel_gemm_moe_q4_k_q8_1_dp4a_bin, kernel_gemm_moe_q6_k_q8_1_dp4a_bin ( #30187 ) 1e6f04a sycl : accelerate MXFP4 MoE with arithmetic decoding and weight reordering ( #29809 ) 10a60cf vendor: apply deep nested json patch from upstream ( #30253 ) 79e2e74 CUDA: fix round issue, under MSVC the CPU and GPU agree ( #30229 ) 8e2d31e graph : reorder get_rows for embeddings ( #30160 ) baef3ed ui: Models Manager Follow-up Improvements ( #30228 ) f39148a llama-bench: respect -fitc if bigger than required benchmark size ( #28331 ) 50e3e3e CUDA: Remove redundant CUDA copies after SSM_SCAN ( #29807 ) 64df918 opencl: fix kernel compilation for a6x GPUs ( #30176 ) 6184e92 model : use exact GELU for ModernBERT encoders ( #30108 ) 8b54361 chat : refactor API ( #30210 ) a518119 llama: keep the backend sampling graph static across ubatches ( #30223 ) 8ae3867 ggml: fix OOB write in ggml_acc with negative offset ( #30135 ) e60eff9 meta : handle host views ( #30217 ) ba6439a webgpu: use 2D workgroup dispatch for all the ops which use 1D dispatch (e.g., rms_norm) ( #30219 ) 5e4878e vulkan: fix rms_norm workgroup count overflow ( #30145 ) 609290b convert : support compressed-tensor mixed-precision NVFP4 checkpoint ( #28636 ) 86a2835 musa: drop mp_21 from the default architectures ( #30203 ) e94acad ci: fix Models Backend webgpu by supporting GGML_OP_DUP ( #30216 ) 5e1d740 ci : set run-name for publish release ( #30214 ) b42b7e6 server: use port 9931 by default ( #30159 ) b013e56 ui : add the models manager ( #29583 ) 2c564f4 ci: fix HIP quality check by ignoring the 320/256 FA kernel spill ( #30212 ) 1f8fa52 metal: add the 128/96 flash attention kernels, accept every compiled pair ( #30209 ) 8a1a9b5 CUDA: pass src1 precision to host MMQ config helpers ( #30168 ) d4d82d6 hexagon: fix IM2COL patch-embed DMA ring overflow ( #30189 ) 3d65c90 sycl : Q5_K reorder-layout MMVQ and fused GLU ( #29375 ) de7fa0a Musa FWHT fix ( #30167 ) 71ad059 CUDA: improve top-k algorithm selection ( #28713 ) a11f57b model : fix DFlash output head sharing ( #30111 ) fc9ce6b CUDA: fix MMQ out-of-bounds reads ( #29953 ) c35b667 CUDA : looped PAD kernel for more than 65535 rows or slices ( #30147 ) 1167d3f CUDA: fix CCCL version guard breaking on major version rollover ( #29453 ) 4f92965 ui: apply ui_settings on first visit in router mode ( #29668 ) c811cb8 llama: support MoE cache over multiple GPUs ( #30112 ) 033df86 server : preserve context checkpoints across slot save/restore ( #26004 ) ff5888f vulkan : fix TOP_K for +inf/NaN inputs and k = 1 on negative values ( #30107 ) dac3087 vulkan: extend sparse FA support to coopmat2 ( #30003 ) 03aa006 vendor : update cpp-httplib to 0.60.1 ( #30134 ) 08246a2 cuda : support arbitrary striding for unary ops on f16, f32, and bf16 ( #29781 ) 46baf1f sycl: FWHT optimizations ( #29605 ) 097f5b5 hex-mmadd: do not assume aligned read/write when bias-add is fused ( #30133 ) d888016 cuda : Use byte strides for roll to allow non-contiguous ROLL operations ( #29547 ) fda1866 CUDA: fix norm family kernels when ne[2]/ne[3] exceed grid dim limits ( #28175 ) ff30363 sycl: fuse the delta-net alpha gate (add + unary + mul) ( #29687 ) 000bee5 sycl: stage bulk uploads (model loading) through a pinned ring buffer ( #29608 ) 37ac634 model : support classifier_activation for rerankers ( #29692 ) 24e4183 ggml-cuda: assign four GDN state columns per warp ( #30087 ) 847f447 sycl: add grouped MoE XMX GEMM ( #29245 ) 75118a3 convert : support Qwen3.5 embedding models ( #27920 ) 9b4ed0c sycl: remove duplicate block-size defines from op headers ( #29507 ) 9c2e0e4 hexagon: enable alloc_buffer_n ( #30126 ) 06cad0b hexagon: Q6_K weight dequant speedup ( #30121 ) a657f7e model : add LiquidAI/d1-omni-600M decision model ( #30114 ) aa5e009 hexagon: support tiled Q4_K and Q6_K GET_ROWS ( #30115 ) 7081510 hexagon: improved GELU accuracy ( #30104 ) bd4eeaa chat: fix jinja parser for TranslateGemma ( #30096 ) 5de7334 chat : name tool and argument parser rules by index ( #30088 ) 88dcc46 model : add LiquidAI/d1-3B decision model ( #30110 ) b86d2f0 cuda: FWHT kernels for block widths above 512 ( #29100 ) 50a6c5c mtmd: add cohere2 vision support ( #30062 ) d6cf9ac llama : add a GPU cache for MoE experts kept in host memory ( #29887 ) 42c787e cuda: update uncoalesced memory reads in pool2d ( #29425 ) 18b5f8b server : accumulate generated text and tokens as parse input ( #29876 ) 448147d llama: share the nextn tensor flags between models ( #30097 ) 9881906 metal : few-row MMA mat-mul for the remaining src0 types ( #30065 ) 7e8324f metal : fix MUL_MAT+ADD fusion when the residual is itself a MUL_MAT ( #30100 ) b9acf13 feat: add GLM5Next MTP, optimize ( #29928 ) 48499d2 qwen3tts : guard speaker_encoder_config patch for CustomVoice variant ( #29179 ) d0b490f sampling : use greedy selection for eligible temperature-zero chains ( #29797 ) 42b021b vocab : add plamo fim tokens ( #30090 ) 7481354 convert : fix token configuration for PLaMo-3 ( #29843 ) ad21565 musa: use the tile lightning indexer kernel ( #30080 ) b7dafa0 vendor : update cpp-httplib to 0.60.0 ( #30081 ) 2690873 imatrix : include clocale for std::setlocale ( #30079 ) 36a7391 ggml-webgpu: fix flash_attn supports_op check for overlapping KV ( #28205 ) fa3c2fa tests : retain the anchor when testing recurrent rollback ( #29923 ) 005a1e1 [SYCL] fix the issue in mixed different model GPUs in FA ( #29071 ) d2a79e6 sycl: accelerate GLM MLA prefill with MKL flash attention ( #29171 ) 5e5b628 sycl : fattn_kv_buffers cleanup ( #27689 ) 4d756bc vulkan: fix amd iGPU slow checkpoint read ( #30049 ) 78651c4 sycl: add IQ3_S multi-column MMVQ ( #29500 ) f498f86 ggml-webgpu: no dawn native features on wasi ( #27069 ) c479922 hexagon: CPY/CONCAT/CONT/DUP overhaul to use DMA/HVX for all cases ( #30067 ) 5ad1c5d cuda : add BF16 support for XIELU ( #29955 ) 51ce9c1 ggml-cuda: use per-thread stream for buffer-init padding memset ( #28782 ) abeada3 vocab : implement PLaMo-3 tokenizer pre-segmentation ( #30045 ) 4625240 model : add K2 Horizon dense and MoVA support ( #29535 ) 3109914 llama: remove the gather path of the glm5-next sparse attention ( #30042 ) 4fbc76d model: support embeddinggemma2 (text+vision+audio) ( #30054 ) 2207c8e opencl: fix OOB read in adreno xmem GEMM ( #30041 ) a46709b RPC: add -sm tensor ( #26610 ) 65840ed ggml: fix CLAMP on non-contiguous views (CPU, CUDA) ( #29517 ) ab09ea4 cuda: BF16/FP16 conversion to f32 chunking ( #29442 ) da263e7 models: support pplx-decider ( #30044 ) a043d38 metal : fix excess threadgroup memory in quantized flash attention ( #29340 ) 58cb913 vulkan : check for null vkEnumerateInstanceVersion ( #29872 ) 4f54067 HIP: use -O0 for host code in debug builds ( #29795 ) f0c41e0 models : consolidate nextn row cropping into shared helpers ( #30017 ) d7a695e scripts : limit apiabi checks to libllama and libmtmd ( #30038 ) 6c73b3e convert : add text_config as fallback [transformers 5.18] ( #30040 ) 1a3011c llama : re-reserve the sched when the nextn extraction flags change ( #30020 ) 6753a03 ggml: refactor selective expert copying to user code ( #29943 ) cbb7d52 test-llama-archs : initialize backends before generating models ( #30034 ) 63bef27 vendor : update LibreSSL to 4.3.3 [no ci] ( #30019 ) b9a5a00 ggml-openvino: fix CI tests; fix GPU regressions. ( #30037 ) 43fe9c6 llama: fix k-pool scatter data race on shared sequences ( #29994 ) 5e03bdd hexagon: ssm-conv updates ( #29971 ) 50569eb hexagon: add pool op support ( #29995 ) 7049ff0 ci : add 1accel label [no ci] ( #30016 ) c250304 ci : skip container re-tagging when Require Docker is disabled ( #30008 ) 8345f33 hexagon: matmul and flash-atten scalability updates ( #29974 )

https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB-GGUF https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB-LiLiCorr-GGUF NVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB is a vision-language model (VLM) designed to run everyday agentic tasks locally, using a native 2-bit quantization to fit in 24GB of memory and run alongside other applications. It optimizes intelligence and speed per GB. It aims to achieve the optimal balance between speed and intelligence, being faster than most popular dense models while smarter than most popular MoE models with a similar memory footprint. This model is ready for commercial or non‑commercial use. Model Summary Total Parameters 74B (9B active) Architecture Mamba-2–Transformer hybrid LatentMoE with a C-RADIO v4-H vision encoder Precision IQ2 (2-bit quantization with quantization-aware training) Context Length Up to 256K tokens Inputs / Outputs Text, images, and video / text Deployment 24GB packed model; vLLM on NVIDIA Ampere (A100), Hopper (H100-80GB), or Blackwell Speculative Decoding LiLiCorr drafter via DFlash; 7 speculative tokens in the companion Bolt vLLM recipes Reasoning Mode Chat-template controls ( enable_thinking=True/False ), inherited from the Super 3.5 VL client Recommended Sampling Text: temperature 1.0, top_p 0.95 (inherited from the Super 3.5 VL client) Best For Running everyday agentic tasks locally License OpenMDW-1.1 Model Overview Model Developer: NVIDIA Corporation Model Dates: December 2025 - May 2026 Data Freshness: The post-training data has a cutoff date of May 2026. What is Nemotron? NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.NVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB is a vision-language model (VLM) designed to run everyday agentic tasks locally, using a native 2-bit quantization to fit in 24GB of memory and run alongside other applications. It optimizes intelligence and speed per GB. It aims to achieve the optimal balance between speed and intelligence, being faster than most popular dense models while smarter than most popular MoE models with a similar memory footprint. This model is ready for commercial or non‑commercial use. bolt! submitted by /u/jacek2023 to r/LocalLLaMA [link] [comments]

Article URL: https://www.simonsfoundation.org/news/simons-foundation-launches-15m-effort-to-help-mathematicians-navigate-ai-era/ Comments URL: https://news.ycombinator.com/item?id=50104654 Points: 237 # Comments: 192

Release v5.20.0 New Model additions Muse Spark Open Muse Spark Open is a multimodal mixture-of-experts model from Meta that takes text, images, audio, and video as input and generates text. It has 816B total parameters (42B active per token) and a context window of up to 1,048,576 tokens shared across modalities. It is trained for agentic coding tasks such as multi-file editing, debugging, and tool use. Checkpoints are available in BF16 and NVFP4 formats. Links: Documentation | Blog Post Add Muse Spark Open ( #49644 ) by @guarin in #49644 Ling 3.0 Ling 3.0 Flash is a hybrid Mixture-of-Experts language model from InclusionAI. Its decoder groups five Kimi Delta Attention (KDA) layers with one Multi-head Latent Attention (MLA) layer. The MLA blocks use interleaved rotary embeddings and a head-wise output gate, and the feed-forward blocks combine routed and shared experts. It is natively supported in Transformers as BailingHybridForCausalLM , so it loads without trust_remote_code=True . Links: Documentation [Model] Add native Ling 3.0 Flash support ( #49446 ) by @icavan in #49446 Kolibri1 Kolibri 1 is Aleph Alpha's mixture-of-experts reasoning model, focused on German and English. Each of its 50 layers routes every token to 6 of 384 experts using sigmoid routing, and adds one shared expert. Four sliding-window attention layers alternate with one full-attention layer, and RoPE is applied only in the sliding-window layers. Links: Documentation Add Kolibri 1 ( #49463 ) by @lucamotz in #49463 MiniCPM-V 4.7 MiniCPM-V is a series of efficient multimodal large language models developed by OpenBMB . Like MiniCPM-V 4.6, the MiniCPM-V 4.7 architecture pairs a SigLIP vision encoder that has a window-attention merger with a Qwen3.5 language model backbone, and supports both 4x and 16x visual downsampling modes. The main addition over 4.6 is canvas M-RoPE : instead of numbering visual tokens along a single 1-D sequence, the model lays every image out on a 2-D canvas and assigns each visual token a (temporal, height, width) position, so slices of the same image keep their spatial relationship and video frames keep their temporal order. Fix more stuff ( #48979 ) by @zucchini-nlp in #48979 UnlimitedOcr UnlimitedOcr is a single 3B-parameter OCR model from Baidu with a standard 32,768-token context length. It builds on DeepSeek OCR and replaces all decoder attention layers with Reference Sliding Window Attention (R-SWA). This keeps the KV cache constant during decoding, so the model can transcribe dozens of pages in a single forward pass. It supports "gundam" mode (640x640 tiles with dynamic cropping) and "base" mode (a single 1024x1024 view), and it can output region detections with bounding boxes. Links: Documentation | Paper Add Unlimited OCR ( #46836 ) by @guarin in #46836 UltraBERT UltraBERT is a family of bidirectional text encoders trained with a combination of masking ratios, pruning, and distillation for classification and retrieval tasks. The released encoders support sequences of up to 32,768 tokens. They use rotary position embeddings, RMSNorm, SwiGLU feed-forward layers, and alternating local and global attention (every third layer is global). Checkpoints include UltraBERT-150M and UltraBERT-380M for masked language modeling and fine-tuning, and UltraBERT-Embed-150M for text embeddings. Links: Documentation | Paper Integrate the UltraBERT architecture ( #49596 ) by @tomaarsen in #49596 MOSS-Transcribe-Diarize MOSS-Transcribe-Diarize 0.9B is an end-to-end audio understanding model for long-form multi-speaker transcription, speaker diarization, and timestamps. It combines a Whisper-style encoder, a 4× frame merge step, a multi-modal projector, and a Qwen3 language model. It produces joint transcription and diarization in a single pass, with segments formatted as [start][S01]text[end] . It supports long-form audio through Whisper-window chunking in the processor, promptable transcription with hotword biasing, and 50+ languages. Links: Documentation Add MOSS-Transcribe-Diarize model ( #48386 ) by @itazap in #48386 Breaking changes 🚨 General breaking notice that inference with EP has regressions in speed; we are working on fixing this ASAP! 🚨 Deprecated checks and methods have been removed from cache_utils.py (affecting models such as minimax_m3_vl and pi0), so any code still calling those deprecated cache methods must switch to the current cache API. 🚨 Remove deprecated cache utils methods ( #49605 ) by @guarin Accidental imports from other models were removed from several model files (e.g. embedding_gemma2, ijepa, lightglue), and the modular converter now avoids adding such cross-model imports. If you imported these names through those model modules, import them from their original modules instead. 🚨 Remove imports from other models ( #49473 ) by @guarin The minimum kernels version is now 0.17.2, which allows use_fallback=KernelizeFallback.ALL so that a failed kernel load falls back to the original implementation instead of raising an error. Upgrade kernels to 0.17.2 or later. 🚨 Bump min kernels to 0.17.2 for better fallback ( #49235 ) by @SunMarc A new SubConfigSpec centralizes the initialization of sub-configs, so default sub-configs are collected from dataclass fields instead of by instantiating the class, and this touches the configuration utilities and many model configs. Custom or existing nested configs may need to be updated to declare their sub-configs through this mechanism. 🚨 SubConfigSpec for centralized init of subconfigs ( #48282 ) by @zucchini-nlp The LUKE and mLUKE tokenizers now treat entity special tokens such as <ent> as special tokens (not split, honored by skip_special_tokens , normalized) while keeping the same spacing as regular vocabulary tokens when decoding. Tokenization and decoding output involving these tokens may therefore differ from before, so check any code or saved outputs that depend on the old behavior. 🚨🚨🚨 luke fixe ( #48408 ) by @itazap Exporters Transformers exporters now support quantization directly during export across Dynamo, ONNX, OpenVINO, and ExecuTorch. The new unified quantization API integrates PT2E, ONNX Runtime, and NNCF quantizers, enabling static and dynamic quantization as well as weight-only compression (INT8/INT4), with configurable calibration workflows. This makes it easier to export optimized models for efficient inference across different backends. For more information see the docs: https://huggingface.co/docs/transformers/exporters_quantization Parallelization This release improves distributed training and inference. FSDP × TP model and optimizer checkpoints can now be saved and loaded across different parallel configurations, and expert-parallel dispatch, tensor-parallel plans, static-cache generate() , and the caching allocator's byte count were fixed. Tied LM heads in composite models now get their own TP plan entry, enable_expert_parallel applies the EP plan alone again, TPU loading uses the `tor FSDP x TP Saving / Loading, Optimizer ( #49256 ) by @michaelbenayoun in [ #49256 ] FSDP x TP Saving / Loading ( #48802 ) by @michaelbenayoun in [ #48802 ] Don't set a device per rank for gloo tensor-parallel tests ( #49636 ) by @IlyasMoutawwakil in [ #49636 ] fix(distributed): keep enable_expert_parallel as EP alone ( #49633 ) by @dacorvo in [ #49633 ] Fix a port allocation race in FSDP tests ( #49455 ) by @icavan in [ #49455 ] Expert-parallel dispatch hands the experts the received rows, not the all-to-all's pending result ( #49421 ) by @IlyasMoutawwakil in [ #49421 ] Fix static-cache generate() with host-side inputs and under TP ( #49317 ) by @dacorvo in [ #49317 ] Composite lm head tp plan ( #49080 ) by @IlyasMoutawwakil in [ #49080 ] Fix: use torch.accelerator device APIs so TP loading works again on TPU ( #49502 ) by @tengomucho in [ #49502 ] Count ep and fsdp in caching_allocator_warmup byte calculation ( #49384 ) by @3outeille in [ #49384 ] Do not wrap the model in FSDP for expert-parallel token dispatch ( #49383 ) by @3outeille in [ #49383 ] Generation This release fixes static-cache generate() bugs with host-side inputs and under tensor parallelism. It also speeds up Whisper generation on TPU by cutting device syncs and recompilation, and fixes Whisper batched timestamp decoding for tensor and NumPy inputs. Pipelines get a cleanup of generation-argument handling that removes spurious warnings. Add job to automatically generate Intel CPU Docker image ( #49560 ) by @regisss in [ #49560 ] Image decoding moves to torchcodec ( #47963 ) by @zucchini-nlp in [ #47963 ] Optimize Whisper generation for TPU and device sync ( #49426 ) by @tengomucho in [ #49426 ] Generation cleanup in pipelines ( #49269 ) by @Rocketknight1 in [ #49269 ] Fix Whisper batch timestamp decoding for tensors ( #49419 ) by @Kingofbooks in [ #49419 ] Vision Vision processing was updated in several places. Image decoding now uses torchcodec, Molmo2's image and video kwargs follow the library's standard conventions, and Pixtral Flash Attention no longer detects false image boundaries after the axial RoPE refactor, which had caused CUDA illegal memory access errors and incorrect output. Several CI and Docker changes also shipped: GPU images moved to Python 3.11, a numpy/pyarrow conflict Fix numpy/pyarrow conflict in the XPU Docker image ( #49582 ) by @jiqing-feng in [ #49582 ] CI: Move GPU images to python 3.11 ( #49352 ) by @tarekziade in [ #49352 ] Update molmo2 processing images/videos kwargs ( #49563 ) by @guarin in [ #49563 ] Fix Pixtral Flash Attention image boundaries after axial RoPE refactor ( #49373 ) by @oliverholworthy in [ #49373 ] Kernels This release improves kernel support across hardware. flash_attention_2 now falls back to the AITER kernel on ROCm when flash-attn isn't installed, and T5-style models get a contiguous SDPA position-bias mask so they use fused kernels again. The Hub rotary kernel is now limited to CUDA inference because its backward pass gave incorrect gradients. The Atlas Inference Gated DeltaNet kernels for Strix Halo and Fall back to the AITER kernel for flash_attention_2 on ROCm ( #49608 ) by @Abdennacer-Badaoui in [ #49608 ] Make the SDPA position_bias mask contiguous so T5-style models use fused kernels ( #49517 ) by @IvenHsu01 in [ #49517 ] Use the Hub rotary kernel for inference only on CUDA ( #49439 ) by @qgallouedec in [ #49439 ] [Atlas Inference] Enable torch.compile for the Strix Halo Gated DeltaNet hub kernel ( #49453 ) by @AzeezIsh in [ #49453 ] [Atlas Inference] Fix GB10 Gated DeltaNet hub kernel for transformers 5.17+ and enable torch.compile ( #49452 ) by @AzeezIsh in [ #49452 ] Cache This release fixes several cache-related bugs: static-cache generate() now handles host-side nested inputs and tensor parallelism correctly, the offloaded cache prefetch no longer races with the offloading copy, and Phi3 LongRoPE cache rebuilds at the context boundary now receive the full prefix. It also counts the query token in MiMo-V2-Flash's sliding window and corrects the quantized cache (hqq/quanto) documentation exam Fix offloaded cache prefetch racing with the offloading copy ( #49363 ) by @qgallouedec in [ #49363 ] docs: update quantized cache hqq/quanto examples and documentation ( #47212 ) by @DavidJohnQuinlan in [ #47212 ] [MiMo-V2-Flash] Count the query token in the sliding window ( #49170 ) by @guapisolo in [ #49170 ] Fix Phi3 LongRoPE cache rebuild at the context boundary ( #49361 ) by @LE0-Lin in [ #49361 ] Bugfixes and improvements [docs] Hardware docs template ( #49575 ) by @stevhliu in [ #49575 ] Add MIC contract lock files for PR CI integration ( #49631 ) by @ydshieh in [ #49631 ] Use aiter-rope version 3 on ROCm ( #49595 ) by @Abdennacer-Badaoui in [ #49595 ] Remove missing methods from docs ( #49637 ) by @guarin in [ #49637 ] Translate torch.mm(..., out_dtype=...) to ONNX ( #49635 ) by @IlyasMoutawwakil in [ #49635 ] [docs] Hardware section ( #49541 ) by @michaelbenayoun in [ #49541 ] Raise when a torch fallback would drop packed-sequence boundaries ( #49474 ) by @qgallouedec in [ #49474 ] Fa packed sequence decode ( #49610 ) by @SunMarc in [ #49610 ] fix nested set_attn_implementation ( #49138 ) by @eustlb in [ #49138 ] feat: Add MTP support to Mellum2/2.1 ( #49531 ) by @shadeMe in [ #49531 ] extend onnx exporters tests to non-cuda devices ( #49627 ) by @kaixuanliu in [ #49627 ] Document video processor backends ( #49470 ) by @DimensionSTP in [ #49470 ] Make sinusoidal position embeddings compileable ( #49482 ) by @jiqing-feng in [ #49482 ] Save the PEFT adapter of a load-time sharded model from its own parameters ( #49500 ) by @qgallouedec in [ #49500 ] fix: Pass the MTP layer type to per-layer-type rotary embeddings ( #49468 ) by @shadeMe in [ #49468 ] Add LFM2 to the GGUF loading path ( #49400 ) by @francip in [ #49400 ] Add MtpModel helpers for frameworks that own the drafting loop ( #49544 ) by @hmellor in [ #49544 ] Build docs with the Python version from .python-version ( #49603 ) by @tarekziade in [ #49603 ] Use mlp_layer_types for AXK1 so the MoE layer layout honours moe_layer_freq ( #49518 ) by @hmellor in [ #49518 ] Combine Nvidia+AMD run-slow CI comments into one ( #49598 ) by @ydshieh in [ #49598 ] Fix flaky streamer timeout tests ( #49583 ) by @tarekziade in [ #49583 ] Fix TPU related tests issues ( #49568 ) by @tengomucho in [ #49568 ] fix(distributed): bind TPU ranks to device index 0, not LOCAL_RANK ( #49543 ) by @tengomucho in [ #49543 ] Use transformers init everywhere ( #49573 ) by @guarin in [ #49573 ] [docs] Context parallelism during eval ( #48663 ) by @stevhliu in [ #48663 ] Remove examples ( #49357 ) by @SunMarc in [ #49357 ] Accept whole-number floats for integer tool arguments in parse_response ( #49547 ) by @yonigozlan in [ #49547 ] update Intel CPU Dockerfile ( #49408 ) by @jiqing-feng in [ #49408 ] Add missing video processor auto docstrings ( #49569 ) by @guarin in [ #49569 ] Add chat input sanitization ( #47386 ) by @Rocketknight1 in [ #47386 ] Fix create_chunked_causal_mask() crashing with block_sequence_ids ( #49457 ) by @kaixuanliu in [ #49457 ] Fix SinglePositionMultiTokenCandidateGenerator signature ( #49413 ) by @jiqing-feng in [ #49413 ] Minicpm 4.7 update integration tests ( #49565 ) by @zucchini-nlp in [ #49565 ] Stop treating HunYuan's DynamicNTKAlpha RoPE as dynamic ( #49445 ) by @jiqing-feng in [ #49445 ] Fix Gemma3nVision2TextModelTest::test_model_parallelism split ( #49562 ) by @tarekziade in [ #49562 ] Fix UnboundLocalError in SuperPoint forward under torch.compile ( #49409 ) by @kaixuanliu in [ #49409 ] Preserve resolved remote backend references in AutoVideoProcessor ( #49511 ) by @DimensionSTP in [ #49511 ] Fix PR slow CI suggestion: install transformers in a venv ( #49558 ) by @tarekziade in [ #49558 ] Fix PR slow CI suggestion: install transformers, fail on script errors ( #49549 ) by @ArthurZucker in [ #49549 ] Remove deprecated data helpers ( #49360 ) by @SunMarc in [ #49360 ] Fix test_all_params_have_gradient for intentionally frozen parameters ( #49264 ) by @thillai-c in [ #49264 ] Flex attn fix for molmo2 test ( #49530 ) by @molbap in [ #49530 ] fix bridgetower cross-attn mask shape bug ( #49447 ) by @kaixuanliu in [ #49447 ] Use Siglip2Tokenizer when the Hub tokenizer_class is GemmaTokenizer ( #49245 ) by @jayzuccarelli in [ #49245 ] Fix mixed BOS prefix validation in WatermarkDetector ( #49496 ) by @dangruiyuan in [ #49496 ] Deprecate unused get_uniform_frame_indices ( #49464 ) by @archiep867-sys in [ #49464 ] [tests] Some fixes for test suite failures on TPU ( #49425 ) by @tengomucho in [ #49425 ] Second phase of torch 2.6 cleanups ( #49396 ) by @Rocketknight1 in [ #49396 ] Guard the checks for current_accelerator() and bump Torch version ( #49395 ) by @Rocketknight1 in [ #49395 ] Use one static Transformers CI badge in PR descriptions ( #49471 ) by @tarekziade in [ #49471 ] fix: treat every declared field of the config class as a default fixes #49456 ( #49466 ) by @tarekziade in [ #49466 ] Keep AutoVideoProcessor class lookup lazy ( #49458 ) by @DimensionSTP in [ #49458 ] Add Molmo2 ( #43451 ) by @SangbumChoi in [ #43451 ] Ci fetches multimodal models recursively ( #49389 ) by @zucchini-nlp in [ #49389 ] Recurse for all subconfigs ( #49461 ) by @zucchini-nlp in [ #49461 ] Aria, Ernie4.5-VL-MoE, Gemma4, Inkling, MiniMax-M3-VL, Qwen3.5-MoE: run the model tests on the VLM/ALM testers ( #49404 ) by @IlyasMoutawwakil in [ #49404 ] Fix CI slack reporting: surface crashed/missing model jobs in model_results.json ( #49427 ) by @ydshieh in [ #49427 ] [Qwen3VLMoe] Update expected text in test_small_model_integration_test_expand ( #49301 ) by @ydshieh in [ #49301 ] Fix backend attribute in documentation and default backend in MistralCommonBackend ( #49149 ) by @samyuktahegde in [ #49149 ] More RoPE simplification, TF32 fixes ( #49328 ) by @Rocketknight1 in [ #49328 ] Complete PR #49228 : remove unused device=None from rope init in 11 model families ( #49285 ) by @ydshieh in [ #49285 ] Revert "[CI] Temporarily disable AMD scheduled CI trigger" ( #49433 ) by @ydshieh in [ #49433 ] Fix QuantizedCache residual flushing for multi-token updates ( #49418 ) by @LE0-Lin in [ #49418 ] Fix data-dependent branch in _ignore_causal_mask_sdpa under compile ( #49354 ) by @dacorvo in [ #49354 ] Fix Inkling moe_intermediate_size when dense_intermediate_size is present ( #49416 ) by @Talhax55z in [ #49416 ] fix bug for DebertaV2ForMaskedLM non-legacy load issue ( #49152 ) by @kaixuanliu in [ #49152 ] This PR fixes Gemma3n fp16 loading when missing weights need to be in… ( #49147 ) by @sywangyi in [ #49147 ] PR CI: grant actions: read to the reusable workflow ( #49417 ) by @tarekziade in [ #49417 ] [Bugfix] [Modeling] Fix DSV4 norm ( #47486 ) by @kylesayrs in [ #47486 ] [docs] Update Spaces ( #49242 ) by @stevhliu in [ #49242 ] handle empty precompiled charsmap in spm converter ( #49211 ) by @itazap in [ #49211 ] [CI] Temporarily disable AMD scheduled CI trigger ( #49394 ) by @ydshieh in [ #49394 ] [tests] Fix flaky CMVN tests in Speech2TextFeatureExtractionWithoutTorchaudioTest ( #49374 ) by @ydshieh in [ #49374 ] Let token dispatch run with ep_size != tp_size ( #49368 ) by @qgallouedec in [ #49368 ] [tests] Skip onnx.exactness for BitForImageClassification (ORT fp32 drift) ( #49366 ) by @ydshieh in [ #49366 ] Significant community contributions The following contributors have made significant changes to the library over the last release: @itazap Add MOSS-Transcribe-Diarize model ( #48386 ) handle empty precompiled charsmap in spm converter ( #49211 ) 🚨🚨🚨 luke fixe ( #48408 ) @ydshieh Add MIC contract lock files for PR CI integration ( #49631 ) Combine Nvidia+AMD run-slow CI comments into one ( #49598 ) Fix CI slack reporting: surface crashed/missing model jobs in model_results.json ( #49427 ) [Qwen3VLMoe] Update expected text in test_small_model_integration_test_expand ( #49301 ) Complete PR #49228 : remove unused device=None from rope init in 11 model families ( #49285 ) Revert "[CI] Temporarily disable AMD scheduled CI trigger" ( #49433 ) [CI] Temporarily disable AMD scheduled CI trigger ( #49394 ) [tests] Fix flaky CMVN tests in Speech2TextFeatureExtractionWithoutTorchaudioTest ( #49374 ) [Docker] Upgrade to torch 2.14/CUDA 13.0 and optimize FA2 build from source ( #49381 ) [tests] Skip onnx.exactness for BitForImageClassification (ORT fp32 drift) ( #49366 ) @icavan Fix a port allocation race in FSDP tests ( #49455 ) [Model] Add native Ling 3.0 Flash support ( #49446 ) @tarekziade Build docs with the Python version from .python-version ( #49603 ) Fix flaky streamer timeout tests ( #49583 ) CI: Move GPU images to python 3.11 ( #49352 ) Fix Gemma3nVision2TextModelTest::test_model_parallelism split ( #49562 ) Fix PR slow CI suggestion: install transformers in a venv ( #49558 ) Use one static Transformers CI badge in PR descriptions ( #49471 ) fix: treat every declared field of the config class as a default fixes #49456 ( #49466 ) PR CI: grant actions: read to the reusable workflow ( #49417 ) @lucamotz Add Kolibri 1 ( #49463 ) @SangbumChoi Add Molmo2 ( #43451 ) @maxiaolong03 [Model] Add PP-DocLayoutV4 Model Support ( #48387 )

FLUX 3 Image Klein is a 5.6B open weights model for text-to-image generation and image editing. It takes a prompt and up to 10 reference images and returns one image. Text-to-image, single-reference edits and multi-reference compositions run through the same model, with Qwen3-VL-4B-Instruct as the text encoder. https://huggingface.co/black-forest-labs/flux-3-image-klein submitted by /u/JGByvygyrfg to r/LocalLLaMA [link] [comments]