model releases

We’re releasing the manuscripts, formal Lean certificates, and reasoning walkthroughs so mathematicians can examine these results and build on their ideas. https://openai.com/index/ten-advances-in-mat

arXiv:2607.29090v1 Announce Type: new Abstract: Postoperative adverse events, including mortality and morbidity, remain a major global burden, many of which are preventable through early identificatio

Cybersecurity chiefs at the White House have reportedly finalized the outline of a forthcoming framework that will enable artificial intelligence companies to voluntarily submit their latest frontier

arXiv:2607.28648v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for emotional support tasks, such as negative thought reframing. This task relies on modifying cogn

arXiv:2607.28699v1 Announce Type: cross Abstract: KV-cache quantization is validated today by offline benchmark averages; a deployed system cannot tell whether compression is damaging the request it i

You probably used @FireworksAI_HQ this week. The cool part is that you just did not know you did. 🎆 Open-source models are free, sure... but the hard part is tailoring them so they perform best on you

You shouldn't need a vision model to know your PDF has checkboxes. LiteParse can now pull structured data directly from your PDFs: form field values, checkbox states, annotations, embedded images, vec

Luz Ding / Bloomberg: Alibaba says its 2.4T-parameter Qwen3.8-Max tops Moonshot's Kimi K3 on some benchmarks, and it plans to release Qwen3.8-Max and Qwen3.8-27B's weights next week — Alibaba Group Ho

All other models on the Portal remain 20% discounted, aside from GPT-5.6 Terra and Luna which are 50% off. https://x.com/NousResearch/status/2080039066771337475?s=20 All models are now 20% off for a l

Another day and another full frontier model running on your computer. Been teething DeepSeek V4 Flash on over 60 employees at The Zero Human Company for a few hours and it is stunning. Between Kimi K3

ggml-webgpu: add support for f16 repeat (#26307) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramew

model : load MiMo V2 MTP tensors only if used (#26412) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XC

sycl: fix classification of iGPUs (#26105) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Li

chat : add qwen3 specialized parser (#26252) Add tagged thinking tool parser chat : refactor and add permute helper cont : add support for <tool_call> omission cont : update tool delimiters cont : add

DeepseekV4 MTP + DSpark (#25784) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux: Ubunt

opencl: bugfix increment ref_count in ggml_backend_opencl_init() (#26162) Incrementing ref_count at the beginning is important later in the free() method of the ggml_backend_opencl_context at program

common: support the DSpark sidecar resolution (#26458) The dspark- files resolve like the other speculative sidecars: the -hfd tag applies to them, a requested sidecar resolves without a full model at

metal: implement DeepSeek V4 hyper-connections (#26459) Implement GGML_OP_DSV4_HC_COMB, GGML_OP_DSV4_HC_PRE, and GGML_OP_DSV4_HC_POST with SIMDgroup register and shuffle optimized kernels. Add Metal d

opencl: limit local workgroup size for GLU operation (#26383) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64)

metal : add F16 support for bin ops (#26465) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework

metal : add SILU_BACK (#25982) feat(silu_back): implemented silu_back op for f32 fix(silu_back): removed redundant asserts in ggml-metal-ops.cpp function ggml_metal_op_silu_back. Website: https://llam

I know qwen 3.5 4b is great but a bit too large and miniPCM5 1b is great for agentic use but not so great for multilingual natural language understanding. Google eXb variants are just too big in total

Mia AI Lab noted on Aug 2 2026 that running DeepSeek v4 Flash via the Hermes agent produces output files superior to those from any other harness tested. The observation was publicly shared and has ga

Hello! VRAM tracker is a node that track the full memory lifecycle of a comfyui run: when each weight is reserved, paged into VRAM, computed on, evicted, and freed. It renders it as an interactive HTM

I let Gemma4-31b run on my laptop for like almost a day using a heavily altered pi to do a deep dive on our beloved Llama tangentially related Subreddit, and this was the conclusion. Feels pretty accu

Cybersecurity isn’t a fortress problem, it’s an immunity problem. Think vaccines. Eliminating pathogen is not practically possible. Vaccines don’t eliminate pathogens. They teach the immune system to

You have two choices here (in order of pref): Downgrade CUDA from 13.3 to 13.1 (skip 13.2 due to bugs) <- prefer this (thanks to u/fairydreaming for pointing this out) Use this vibed fork that works w

DeepSeek V4 Flash 0731 is impressive. It feels like a capabilities leap for a model in this size class. It is also incredibly cost efficient. The model is available in Bionic, both for running locally

Hello, Also I want to join the hype of posting token specs. CPU: 2x Intel Xeon CPU E5-2650 v4 @ 2.20GHz RAM: 2x 4 Channel 2400MHz DDR4 GPU: 1x AMD Radeon 7900 XTX 24GB 3x AMD Instinct MI60 32GB Strang

Hello everyone I want to join the hype of posting specs. CPU: AMD EPYC 74F3 24-Core RAM: 8 Channel 3200 DDR4 GPU: RTX A6000 48GB Prompt processing is in the high 70t/s (got down to mid 30t/s at 300k c

I decided to test a few questions against DeepSeek-V4-Flash-0731. Locally, I was running Unsloth's UD-Q2_K_XL quant. After I saw the surprising shape of the results, I tested against DeepSeek's offici

Following up on my Qwen 3.6 port, I wanted to keep adding models and ended up fixing a bunch of things along the way, so it's its own engine now: Mference. Same core idea from TurboFieldfare, MoE mode

Don’t think I have seen more misconceptions around one model (I count 8) since the fantasies people had before GPT-5 was released. That turned out to be a letdown; so will Astra, for people who are dr

TensorSharp supports DSpark on Deepseek v4 Flash 0731 now. Here is the benchmark result on 4x Nvidia A40 GPUs, cuda 12.8 with/without DSpark: Model: DeepSeek-V4-Flash-0731-UD-Q8_K_XL from https://hugg

Emad Mostaque @EMostaque came on PostAGI and said AI had already found 121 years of missing algebra in Einstein's equations. A billion parameter model trained on nothing past 1911 got to general relat

I love the progress happening on open models but I feel like it is kind of getting clear that hardware to run good sized models is completely unaffordable for me right now. I know that you all love Qw

Hey all, tldr / who this helps: you run a mixed multi-GPU box where the experts spill to RAM, and you want to stay in the 3-bit tier instead of dropping to Q2 to make it fit. https://huggingface.co/Ta

Hi everyone, I’m fairly new to the multi-GPU side of local LLMs and I’m trying to understand how inference actually scales across multiple GPUs. Suppose I have a model running on a single GPU and then

Hey r/ollama 👋 If you run Ollama locally alongside cloud endpoints for agent workflows, Cursor/Windsurf, or custom scripts, managing API switching, failover logic, and context limits can get messy fas

The June edition of my sponsors-only monthly newsletter is out. If you are a sponsor (or if you start a sponsorship now) you can access it here. This month: Accidental cyberattacks by OpenAl and Anthr

here is all the info we have based on open PRs to add support to ComfyUI and HuggingFace diffusers - 33B for the main DiT and a pruned 20b variant - Qwen-3-VL-32b as the text encoder Edit- I posted cl

Open letters about AI development I wrote this summary of the past few weeks of open letters as a section of my sponsors-only newsletter but I've decided to share it here as well. Open Weights and Ame

GPT-Live is so good that I use it almost every day. I've been wanting to replicate it since it was released. My first attempt was to fine-tune Gemma 4 12B to behave like a full-duplex model. Something

DSv4F doesn't ship a jinja, but for distributions that do and faithfully reconstruct what DS releases in their chat template python, every system message is hoisted into the system prompt at the top -

https://llama.app/ Been using llama.cpp for years now and im on here all the time (im a mod..), but somehow I totally missed that llama.app exists and its official from the HF/llama.cpp team. So posti

Hey y'all. I'll be concise. TL;DR: DS V4-Flash-0731 @ UD-IQ2_M running fully in VRAM on 3xMI50s (90.9 GB model, 96 GB VRAM). Actual speed on llama-server is: - Text Generation: ~15-16 tokens/second st