Knowledge catalogue
I built SpeakoFlow Mini, an Apache-2.0 fine-tune of Qwen3.5-0.8B for dictation cleanup. It is not a chat model or a general rewriter. It takes speech-to-text output, applies corrections the speaker ac
LLMs will always be bulls in a China shop. Nailed it, December 2022. We are still suffering the consequences. And they are about to get so much worse. No wonder Silicon Valley tried for so long to dep
I am doing a lot of translation work with different languages, and German is just exceptionally well done by Qwen 3.8 27B. It is lengths ahead of GPT-5.6 and even Fable 5. With the frontier models I o
Running on a M1 Max 64 GB: - SSD streaming for tensors - SSD streaming for engrams - SSD streaming for MTP How is it possible? * Custom Q4 quant: benchmarked all metal tensors then picked and spliced
Setup: MacBook Pro M5 Max, 128 GB unified, macOS 26.5.2 · llama.cpp b10686 (Metal, 12 threads, batch 2048, flash-attn, kv-unified, ngram-mod spec decode) · Qwen3.8-Flash-Next UD-Q2_K_XL (Unsloth), 78.
Been working really hard for the past month to bring to the community all these models, the hardest was for sure LongCat-Flash-Lite-Sparse who required TONS of work, first I needed to have Heretic sup
OpenAI announced ChatGPT Work on July 9th, and have been furiously iterating on it ever since. It is an extraordinarily confusing and very powerful product. Here's what I've figured out about it so fa
I find both Qwen 3.8 27b and Qwen 3.8 Flash Next difficult to read. Here's some examples of what I mean: **Model-visible tool set per turn** (assembled by the host at provider-request time): persona t
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
I have been using the unsloth/Qwen3.8-27B-NVFP4 with 157k ctx on vllm currently. But recently I have been seeing post about ninfer lately and how it is like the best way to run qwen 3.8 on RTX 5090 32
Hey all, You’ve probably noticed that using LLMs and AI agents for pentesting has become pretty common lately. The problem is there isn’t really a good way to figure out which model is actually best s
// Your LLM judge disagrees with the experts // LLM Judges can be tricky to build. Here is an interesting showcasing why: There propose a reference-full benchmark of hundreds of complete human-to-huma
I got a 50% performance boost (20 t/s -> 30 t/s) in llama.cpp for MoE models that don’t fit entirely in VRAM—in my case, Qwen 3.8 Flash Next. The idea is simple: instead of offloading entire layers to
Anthropic recommends that users clear or update their **CLAUDE.md** file after each new model release, ensuring the documentation reflects the latest changes. The message includes an informal comment
@claudedevs: Anthropic will “permanently” raise weekly Claude Code limits by 25% on Sept. 14 for most plans, which will work out a 17% reduction, given the current 50% boost — Starting September 14, w
You guys remember sesame AI? With their really good AI voice model? Obviously ChatGPT has their voice model that’s also really good. Is there any smaller local variant that runs on like consumer grade
specs hardware: M4 Max 128GB Studio inference engine: llama.cpp (qwen4exp branch) judge: claude-opus-4-6 AtomicChat/Qwen3.8-Flash-Next-GGUF Qwen3.8-Flash-Next is a great model I benched in my previous
vulkan: Change mul_mat_id to pad K rather than N (#27925) The N padding is needed for mul_mat, but not mul_mat_id. For mul_mat_id, we indirect the row index through a shared memory lookup table which
metal : add fa-vec tunings for M1 Max (#27932) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/43859747 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple S
vulkan: combine duplicated fastdiv functions, rename the one optimizing small divs (#27526) vulkan: combine duplicated fastdiv functions, rename the one optimizing small divs remove one more fastdiv W
sycl: make --fit respect --fit-target better (#27629) improve the --fit algorithm to take into account the actual peak required VRAM for a given context size on a SYCL backend. This includes both prop
metal : add remaining fa-vec tunings for M4 Pro (#27915) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/43865923 macOS/iOS: macOS Apple Silicon (arm64) mac
metal : assert shared memory padding (#27951) metal : assert shared memory padding cont : add ref Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/43867822 m
opencl: use a better matmul path on two Adreno GPU generations (#27640) opencl: default the Adreno xmem F16xF32 GEMM on for X2E kernel_mul_mm_f16_f32_l4_lm is the slowest matmul this backend has on Ad
Banger paper from Apple. If you build MCP servers, this can help you turn your specification into an evaluation suite. (bookmark it) It's actually a really neat idea that's easy to implement. And it s
GLM 5.3 and GLM 5.3 Flash (previously Ox Alpha) are fully rolled out on Ollama's cloud. Private. Fast. US and Europe hosted. Zero data retention. GLM 5.3: ollama launch claude --model glm-5.3:cloud GL
GLM-5.3-Flash is live on Fireworks on day… 2 Why? Because we take quality very seriously. We found a benchmark discrepancy we couldn’t explain, so we delayed the launch to investigate. Day 0 (Wed): we
I absolutely love Ollama for the majority of my inference needs. Starting to roll out Rehex to friends, and I've gotten requests to support OpenCode Go and Codex. Frankly, I'm really excited to add th
In mid-August, Ramp published spending data collected from 70,000 U.S. companies: Fable 5 ,the most powerful and expensive model in Anthropic’s lineup, accounts for just 11% of what those businesses s
I have a 2 DGX Spark setup recently and I have been happily running Deepseek V4 Flash 0731. Since the release of GLM5.3 Flash and Qwen 3.8 Flash Next this week, a lot of folks are still waiting to see
Introducing Hy4 Preview New open weight text input (no vision) LLM from Chinese company Tencent today: 770B total parameters, 49B active parameters, 1M token context window, 1.56TB on Hugging Face. Th
Hardware: rtx 5080 16 gb vram; 64 gb ram ddr5 6000hz; ssd with unlimited memory; ryzen 7 9800 x3d. OS: Windows 11 Software: I’d prefer llama.cpp, but it’s not a strict requirement; I’ll use whatever y
People who moved from older version to this new one, how much improvements do you see on inference? Ex: llama.cpp performance? This new version comes with Linux Kernel 7.0. Hope AMD cards gonna enjoy
Folks! We're just 50 PRs away from more faster inference. Hopefully by end of year. Experts!, please chip in there. List of Open/Ongoing PRs(and also Discussions) related to CPU/RAM/Disk/Hybrid: [Disc
Long-time admirer of the Cursor team, few have done more to bring AI coding to the world. Excited to continue to partner with them. Cursor has been a trusted partner of Anthropic since Sonnet 3.5. We’
I kept running into the same thing. Change a system prompt, get a different answer, and I couldn't tell if that was my change or just the sampler. The loop made it worse. Edit the Modelfile in another
// Memory is what breaks long-horizon agents // It's undeniable how important memory/recall is for long-horizon tasks. If you are building for long-horizon tasks, this is a great read. (bookmark it) T
Just FYI. They removed it. Free models that can be used with Ollama Cloud now: gemma4:31b gpt-oss:120b gpt-oss:20b nemotron-3-nano:30b nemotron-3-super nemotron-3-ultra submitted by /u/IndividualPlus2
TL;DR: Every public low-bit GGUF of this model is secretly ~4.70 bpw. Shim the rows to 256 and it becomes a real 3.07 bpw / 11.77 GiB file that runs 262K context on 16GB. Needs patched llama.cpp — not
Hey! I forked NInfer (a from-scratch C++20/CUDA inference engine for Qwen models) and added two things: tensor-parallel across two GPUs, and YaRN ×4 rope scaling. Together they let Qwen3.8-27B NVFP4 r
happened simultaneously across the board. OpenCode, OpenRouter, and Ollama all ruined DeepSeek at the exact same moment. They literally turned a brilliant model into a complete idiot overnight. I abso
I wanted to share my successful setup for running a Qwen 3.8 27B model with a massive context window on a consumer 16GB GPU (RTX 4070 Ti SUPER). The goal was to fit everything into VRAM without sacrif
I ran a few benchmarks on my MacBook Pro M5 Max with oMLX: Running Qwen3.8-27B with thinking off has a big effect on output quality. Running it with thinking on xhigh burns 5.5x the tokens and runs 6x
Below are the benchmark results for running Qwen3.8-Flash-Next on Strix Halo using the Vulkan backend of llama.cpp, combined with MTP model. Hardware Item Details CPU AMD Ryzen AI MAX+ 395 (16C/32T) G
~20–30GB smaller than Unsloth/AesSedai Q4 at similar PPL. After several days of testing I released a set of mainline-compatible imatrix quants for Qwen3.8-Flash-Next. Goal: same quality band as the po
this model is really something incredible, saved us like 600 dollars. my fiance is always breaking her electronics and then getting me to fix them. The other day she brought me her phone and it was st
Tim Ingham / Music Business Worldwide: Sony Music and Warner Chappell sue Anthropic, Dario Amodei, and Benjamin Mann, alleging tens of thousands of copyrighted songs were used to train Claude's LLMs —
Announcement: https://www.tbench.ai/news/terminal-bench-4-0 Leaderboard: https://www.tbench.ai/ Imo the best aspect in their announcement is their focus on rapidly iterating on TerminalBench to keep t
The official benchmark card for Ling-3.0-flash-Fin is a useful reminder that the unit being tested is rarely just “the model.” The release says most runs used temperature 1, top_p 0.95 and the highest
This paper does a good job of showing the promise and gaps of autonomous AI scientists. Big question is how much more advanced models close those gaps. Today, we're excited to share early progress on
Users are encouraged to try the newly released GLM 5.3 through the `ollama` platform, as announced by @ZixuanLi_ on Twitter. A new benchmark release—Terminal‑Bench 4.0—was also dropped, highlighting t
I was running Ollama with Maxwell 1500, using a 65,536-token context window, through Zoo Code inside VS Code. I asked the model to analyze a folder containing some pretty simple code. It spent around
hi i make tool for this called llmog it's purpose to make llms free to - auto annotation datasets - reclassification existing yolo datasets running totally local using llama cpp or vllm or use externa
We’re ending our partnership with Cursor following its acquisition by SpaceX. Under our proposal, Cursor’s direct access to our models would end on November 12. We know that the people most affected b
Qwen 125B/6B models with a 1‑million‑token context window and multimodal support have been released on Alibaba’s OpenCode Go platform. The new Qwen3.8‑Flash variant is also now available in the same e
arXiv:2608.26947v1 Announce Type: cross Abstract: Embodied agents need environments that are visually diverse, physically interactive, and changing over time. Procedural simulators can generate large
arXiv:2608.26234v1 Announce Type: cross Abstract: Retail electricity markets in deregulated systems face significant price volatility and complex interactions with forward and futures products, posing
arXiv:2608.26926v1 Announce Type: new Abstract: Small language models (sLLMs) are nowadays hosted on devices with limited memory and computational budget. In an autoregressive setup, inference is memo
arXiv:2608.26177v1 Announce Type: cross Abstract: Long-form generation exposes fundamental limitations of large language models. Even 70B-parameter models exhibit length collapse at 16k-token outputs,
arXiv:2608.26618v1 Announce Type: cross Abstract: Model selection becomes particularly challenging under strong predictor dependence and model-class uncertainty, especially when there are exponentiall