Search: “multimodal”

LTX-2.3 is a multimodal video generation model developed by Lightricks that generates synchronized audio and video in a single forward pass at resolutions up to 4K at 50 frames per second. The model i

SenseNova-U1 is a native unified multimodal model built on the NEO-unify architecture that eliminates visual encoders and VAEs, instead using a near-lossless visual interface that preserves semantic s

LTX-2.3 is a multimodal video generation model released by Lightricks in March 2026, available in four checkpoint variants including a distilled variant that completes generation in as few as 8 denois

I'd like to search for information about this specific Reddit post to provide you with an accurate summary based on actual content rather than assumptions. SenseNova-U1 is SenseTime's unified multimod

SenseNova U1 is an open-source unified multimodal model that integrates understanding, reasoning, and generation within a single architecture, with particular strength in creating complex infographics

DeepSeek V4 is undergoing limited grayscale testing with a new interface featuring Fast, Expert, and Vision modes . The Vision version represents the multimodal component of the upcoming DeepSeek V4 r

MiMo-V2.5 is Xiaomi's multimodal AI model with native visual and audio understanding that supports up to 1 million tokens of context. The GGUF format refers to quantized versions of the model optimize

SenseNova's NEO-unify is an encoder-free unified multimodal model built on a Mixture-of-Transformers (MoT) backbone that eliminates the need for traditional VAE encoders in image generation. The 2B NE

This Reddit thread on r/ollama discusses running Meta's Llama 4 Maverick — a ~108B parameter model — locally using Ollama. Llama 4 models are natively multimodal AI models supporting text and image un

Hi, I see that vllm released d3 (a family of decision models) and llama.cpp already has support for them. I want to try them out. Are decision models sensitive to quantization? I know that for autocom

Follow-up to my post from about two weeks ago. A lot has changed since then, so I wanted to post an update on where the backend is now. Where the green architectures stand When I posted last time, 7 a

IQuest-Q1 is a Mixture-of-Experts (MoE) model developed by IQuest for agentic coding, reasoning, and multi-step tool use. It comprises approximately 320B total parameters, with an estimated 15B parame

Model Overview Ling-3.0-flash-VL inherits the language, reasoning, and long-context capabilities of Ling-3.0-flash, while extending them with native image and video understanding. The model has 124B t

Ming-Image-0.1-Design creates complete UIs, dashboards, infographics, and posters from structured prompts of up to 8K tokens, with consistent layouts, typography, colors, and imagery. It can also nati

I've been working on something that started as the training backend for my Hierarchos architecture, but it has grown into a much broader project: https://github.com/necat101/Hierarchos-Native Hierarch

VLLM Benchmark: Prefill, Prompt processing - avg, 871.93 tok/s (3 hours constant running xhigh) - 10K prompt, 1000.26 tok/s (16 runs) - 90K prompt, 743,59 tok/s (16 runs) Decode, tok gen - avg, 38.39

Original Source from DeepSeek WeChat Official Account: https://mp.weixin.qq.com/s/qg0NU3NNUbp1co2PdkAPAg Today we're officially releasing the DeepSeek V4.1 Flash model. It is the smallest model in our

I'm testing a new approach for reducing the cost of image-based LLM inference. I evaluated it on the MOMA Graph benchmark, using 1,315 questions. Compared with using GPT-4o to process the original ima

Ling-3.0-flash-VL inherits the language, reasoning, and long-context capabilities of Ling-3.0-flash, while extending them with native image and video understanding. The model has 124B total parameters

similar to video upscaler LLMs, what's the bleeding edge right now on something similar for audio music? the idea is you feed it in with a low bitrate MONO old soundtrack, and the LLM then recreates i

Here's how it works in the app: Framework: Runs on LiteRT-LM (like Google's AI Edge). Models: Downloads either the E2B (~2.5 GB) or E4B (~3.6 GB) INT4 quantized models directly from ungated litert-com

Hi r/LocalLLaMA 👋 Today we’re excited to release Muse Glimmer, a 30B open-weight model built specifically for local agent workflows. We’re releasing the weights to the community under a permissive Apa

Just saw this posted by Fal.ai and then by Hailuo themselves, the next video model will be open weight released! Here's the blurb and link to to the feature post: Today, we're launching MiniMax H3, a

GigaChat Audio 10B is an audio-native LLM built on top of the GigaChat 3.1 Lightning text model. A Conformer speech encoder and a modality adapter feed audio embeddings directly into a Mixture-of-Expe

Cuda and Vulkan Benchmark: TensorSharp vs. llama.cpp I would like to share my latest open source local Unsloth (GGUF) LLM inference engine and applications. It supports many models from Unsloth, like

https://bfl.ai/blog/flux-3 “Over the next few weeks and months, we will make the following capabilities available, each after an early access phase for ensuring smooth rollout, collecting feedback and

INT3 compression with fused Metal kernels enables large language models to compute attention operations directly on compressed (INT3/INT4) key-value cache representations using custom GPU kernels that

Google DeepMind released MedGemma, a collection of medical vision-language foundation models based on Gemma 3 in 4B and 27B parameter sizes, demonstrating advanced medical understanding and reasoning

This Reddit post discusses a known instability in PPO when advantage estimates operating across different temporal scales (e.g., short-horizon and long-horizon returns) are dynamically routed or mixed

Disclaimer: I work for NuMind, the company behind this plugin. TLDR: Text/image (simili-)embeddings from the LLM you already have loaded :-) I've been working on making it easier to obtain embeddings

indexes PDFs, Office files, scans, screenshots, audio and video, and finds them by content. A query like 'when did they decide to freeze hiring' opens the recording at that second. * EmbeddingGemma 2

Hey r/LocalLLaMA! Maintainer here. Some of you might remember OpenMed from our 1.8 post a few months back. Quick recap: it's an open-source (Apache-2.0) medical AI toolkit with one rule we never break

Hey everyone! 👋 Feeding multi-gigabyte files (SQL dumps, videos, audio, PDFs, and spreadsheets) directly into LLMs often causes context window overflow, GPU VRAM bottlenecks, massive token bills, and

DigUp is a free Mac app that runs Google DeepMind’s new EmbeddingGemma 2 locally over your own files. The model puts text, images, audio and video in one space, so you describe what you remember and l

https://preview.redd.it/owhrvbbiu2uh1.png?width=4096&format=png&auto=webp&s=55d816753e7adee0e1391c9f581052b4bde44da7 d1-omni-600M d1-omni-600M is a 600M parameter decision model built on LFM2.5-Encode

I constantly see recommendations saying that ik_llama.cpp (ikawrakow's fork) is the undisputed king of hybrid CPU/GPU offloading and MoE performance. However, every time I benchmark it against mainlin

An agentic model from Microsoft for the GPU poor https://huggingface.co/bartowski/FrogNano-4B-2609-GGUF FrogNano is derived from Qwen/Qwen3.5-4B, a general-purpose post-trained model designed for lang

'AREX-2 is a 27B-parameter long-horizon agent model from the Beijing Academy of Artificial Intelligence (BAAI). It learns to improve a solution over multiple test-time rounds: propose, measure, reflec

Yesterday I released Bonsai-Llama-Jev - a Jev-like Typed Decision inference system for locally operated, sovereign AI. In my new typed-decision-bench, covering more than 22,000 decisions across 175 us

I've been comparing and working on Gemma 4, Phi-4-Mini, and Qwen3-4B for local LLM use on an 8 GB Raspberry Pi 5. The results were indeed close, but each model has a different strength: Gemma 4 E2B: a

EDIT: Thanks from the Waymo AI Team! A huge thank you to the r/MachineLearning community for all the thoughtful, deep-dive questions today. If you want to dive deeper into our technical research, chec

TL;DR: switching KV cache to f16 may give a boost in speed if using MTP and ngrams. I have a self-built 'AI mega-cluster' with 2x P40s on a cheap Chinese motherboard and a Xeon CPU (around $1,100 to b

I'm planning to upgrade my workstation (linux with 5700X/64GB DDR4) for local inference and pytorch training. I'm trying to decide between: 2× AMD Radeon AI PRO R9700 32GB 2× AMD Radeon PRO W7800 48GB

If you are thinking what model will fit best your HW specs and tasks you are doing here is one table with all currently popular models that still can be considered as local. LLM Test Scores Feature De

I’ve been experimenting with MiniMax H3 and finally have video generation working in TensorSharp. TensorSharp started primarily as a local GGUF/LLM inference engine, so getting a video-generation pipe

We used HFlow to evaluate the latest open weights VLMs for processing egocentric data. This was based on Build AI's Egocentric-10k evaluation, which used Gemini 2.5 Flash to measure hand visibility an