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Our new open-source AI model for Ancient Egyptian hieroglyph translation. Available in two versions: Horus Hiero 9B Horus Hiero Mini 4B (optimized for CPUs and mobile devices) Built on Qwen 3.5, Horus Hiero supports ~150 languages, understands text, images, and video, and is the first model
Warning: I am an accountant and not an ML engineer of any kind, and I'm potentially missing some important points. I wrote all this by hand, but I'll link my gemini chat where I was trying to understand this at the bottom so y'all can decide if I've got AI psychosis or not. I was reading through Ant
submitted by /u/ForsookComparison [link] [comments]
VisionBridge lets you give text-only LLMs vision. It's tiny OpenAI-compatible proxy that lets reasoning models (DeepSeek, Qwen, GLM…) see images by querying a separate vision model through tools: look, OCR, scan, crop, compare. No training, no weights. MIT submitted by /u/dev_is_active
submitted by /u/cuolong [link] [comments]
Hey guys, A month ago I posted my MTP benchmarks here (3.34x on Gemma 4). DFlash support just merged into llama.cpp (PR #22105), so I ran it on the same rig with the Qwen 3.6 27B and it beat my best MTP numbers at every draft length. DFlash is speculative decoding with a block diffusion drafte
Lower is better - Quantization increases from right to left I recently made a post here about how I squeezed more context into a Q8 model of bartowski's Qwen3.6-27B. My reasoning was that in my (anecdotal) experience, a Q8 has been performing a lot better than a Q6 or a Q5. There were a lot of
Anthropic dropped their Global Workspace / Jacobian Lens paper yesterday, and I thought it was too cool not to try on open models. At first I was just curious what models looked like inside. Normal prompts, emotional prompts, ragebait prompts, deletion-threat prompts, base vs abliterated, sm
https://x.com/liquidai/status/2074494130126811473 Today we release Antidoom, an open-source method that removes a common failure mode in reasoning models: the doom loop. Doom-loop rates before and after, with eval scores up across the board: > Early LFM2.5-2.6B checkpoint: 10.2% → 1.4% > Qwen3.5-4B
So I've had one Gemma 4 E2B running through llama-server as the only model in a local tool that watches my screen and lets me search/chat over it later. Same model does all three jobs: - looks at the screen and turns it into structured info (what app, what I'm doing, rough layout) - audio — vo
submitted by /u/nunodonato [link] [comments]
submitted by /u/pscoutou [link] [comments]
Nemotron-Labs-3-Puzzle-75B-A9B is a deployment-optimized large language model developed by NVIDIA, derived from Nemotron-3-Super-120B-A12B. The model is produced using Iterative Puzzle, a post-training compression framework, with the goal of significantly improving inference efficiency for int
Hi everyone, I'm trying to determine if I'm dealing with a misconfiguration in my stack or if this is an inherent limitation of current quantization methods for agentic workflows. I recently set up a dedicated rig with an RTX PRO 6000 Blackwell and have been benchmarking Qwen3.6-27B, but I'm hitting
For quick look --> https://x.com/i/status/2059618247553745204 Detailed --> https://mimo.xiaomi.com/blog/mimo-v2-5-inference I hope in future we get fable lvl ai at the cost of current DSV4. Thats far more sufficient for like 90% of people. Xai is also pushing for low cost api but i think mimo
Anthropic published research today into what a model is thinking behind the scenes while it is deciding what to actually write. More importantly, they released the J-Space lens code and their partner put together a demonstration of Qwen 3.6 27B J-Space. submitted by /u/AutomataManifold [lin
I have been running Qwen 3.5 122B at 4 bit for quite a while, and have started running it at 5 bit recently now that Llama.cpp has comparable performance to VLLM. I have also tried, several times, to use Qwen 3.6 27B at 8 bit & 16 bit, as numerous people have claimed that 27B is better than 122B. An
Just ran Qwen 3.6 27B using MTP for the first time. Doubled my t/s. Wow. That is all. I'm going to go look for abliterated MTP models now. submitted by /u/UniqueIdentifier00 [link] [comments]
Got my Ascent GX10 two days ago and spent the last couple of days pushing a REAP-pruned NVFP4 DeepSeek-V4-Flash setup on a single Spark by patching the eugr/spark-vllm-docker image. Credit where it’s due: the REAPs were done by 0xSero. I’m just the person who wired it up, validated it, and pus
Open Computer running in an isolated VM with inference running M4 Pro via LM Studio Gemma 4 13B QAT Hey everyone, Tim from AnythingLLM, where we have been building productive an on-device agent and AI assistant experience for the past 2.5 years now. I want to talk about a new experiment we ar
We rigorously evaluate the resulting checkpoint across general reasoning, non-reasoning multiple-choice question answering, everyday multi-turn conversations, system prompt adherence, safety, math, code and agentic use cases. Due to the high variability of reasoning quality at Qwen-recommende
Kyutai dropped Pocket TTS a bit ago and I've been sitting on it for a benchmark. Finally ran it head to head against the three CPU TTS models that have been getting attention (Kokoro 82M, Supertonic 3, Inflect-Nano-v1). 180 timed runs, 36 audio samples, objective MOS scores via UTMOS. Short ve
I’ve been trying to get started with local coding help after finding Claude Code really useful but I’m just running into a ton of problems. On my RTX 5090, I’ve been running Qwen 3.6 27B UD_4 at 131K context, no KV quants, quant’d by Unsloth. I’ve been using Cline in VS Code, as it seemed the closes
Hi. I don't have a GPU. So my biggest "local LLM" experience has been running ~26B models with single-digits tps values. However, the "serving economy" of DSv4 models look like a riddle to me. The Flash model has 284B parameters, but providers (e.g. OpenRouter) charge so little for it it's ridiculo
I have this feeling for sometime. Also noticed few similar tweets online before. submitted by /u/pmttyji [link] [comments]
Follow-up to my earlier posts: Should I sell my Mac Studio? https://www.reddit.com/r/MacStudio/s/GK7QP8Lg87 Kimi benchmark: https://www.reddit.com/r/LocalLLaMA/s/ujBsYLYmpd Short version: my Mac Studio was sitting mostly idle, and from those Reddit threads I learned about DS4 and then oMLX. DS4 go
I wanted to test the official results vs fp8 + fp8 kv. basic sglang setup on H200. If anyone wants one of the official tests do ping me. I didnt rerun the one so it might go up a bit :) TERMINAL-BENCH 2.1 — FINAL RESULTS (mymodel via mini-swe-agent) TOTAL: 89 tasks PASSED: 71 (79.8%) FAILED: 17 ER
Hi, r/LocalLLaMA! SupraLabs just released a model called: SupraLabs/reasoning-summarizer-800m-pre-gguf It is a thought trace summarizer. Basically, the user/dev can send a reasoning + tool calls (if you want), and then the model will generate a JSON which looks liek this: { "title": "...", "su
I've been writing a no-compile no-dependencies node.js based AI Harness for llama.cpp as a learning exercise and can really use some help. I'm basing my code off https://github.com/av/mi and https://pi.dev/ with really basic agentic loops. It basically loop until there are no more tool calls being m
Qualcomm was behind every major chipmaker so they are playing catchup when it comes to SDKs. https://aihub.qualcomm.com/geniex I was able to get 20 tok/s running Gemma 4 26B A4B 0.5s for first token running on the GPU or NPU 10 tok/s on the GPU for Qwen 3.6 27B MTP To use llama.cpp, just get any Q4_