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I was reading up on the recent controversy around Tristan Buckmaster, Levent Alpöge, OpenAI, and the Navier–Stokes result, and it got me thinking about something broader than this particular dispute. Buckmaster says that he and Alpöge had been putting drafts from their project into Codex while worki
I don't think anyone posted about this here, but Qwen released a finetuned version of 3.5 4 for driving. The full Bf16 checkpoint is 9B. This is a very interesting development of Chinese AI labs tackle self driving next with open weight models. Edit: the HF repo links to the github repo, which
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, with only 5.5B parameters activated per token, and supports a context window of up to 1M tok
Privacy have been concern of many of us to have their own hardware to run llms, and here's another reason why: two mathematicians spent a year cracking one of the hardest problems in math and fed every draft of their works into Codex. A few days before they could publish, OpenAI suddenly showed up w
Found this post:https://www.reddit.com/r/ClaudeAI/s/At9QNEbawK of someone fully developing a game through AI writing code, generating 3D models, etc. (he is using Meshy to create the 3D models). I was curious about creating something similar with Hermes Agent (or other?) that would develop ver
We’ve been building PipesHub for a while now, and I’d love to get more teams to try it and tell us where it breaks. The problem we kept running into was pretty simple: Building an AI app over company data looks easy in a demo. Connect a few sources, chunk the documents, throw them into a vector DB o
Which small model are you ppl finding the most useful rn? They all seem to compete for the same size class while actively avoiding each other in benchmark tables. Edit: These three in the title and also MiniCPM5 2b, Spark 1.7b, and LFM2.5 2.6b and LFM2.5 8b submitted by /u/Hot_Example_4456 [
Translation: "Internal beta testing for an intermediate version of DeepSeek V4.1 Flash is now open; you are welcome to try it out. It adopts a new model architecture featuring native multimodal support, stronger capabilities, faster speeds, and lower costs. Keep your base_url unchanged and set
I’ve been thinking about this after using more agentic/local coding models. A lot of the newer models are surprisingly good at continuing on their own. But sometimes that seems like the problem. If a requirement is ambiguous, I’d rather the model stop and ask: “Do you mean A or B?” instead of spendi
I found a lot of room on the table for these cards so I decided to make a specialized build to squeeze all I could. first The results: qwen 3.8 next Q3_K_XL: 920tk/s pp8192 (2 cards, ram offload), 24/27 tk/s on prose, 40+ tk/s on code with MTP but without MoE expert cache (which IS included if yuo w
Gemma 4 12B runs on an RTX PRO 4500 Blackwell. Gemma 4 E2B run on a Jetson Orin NX 16GB; similar performance is expected on a Jetson Orin Nano Super 8GB. Both systems use a reSpeaker Flex 4-mic array and a 3W speaker. Inference is handled by Cortexist Little Gemma, a small LLM engine written i
Please, Oh gentle soul, please beautiful soul, please kindhearted soul, with serious hardware and time to kill, please benchmark Unsloth Qwen3.8-27B-Q8_K_XL against Qwen3.8-Next-Flash-Q4_K_XL and provide the definitive answer to my question: IS IT WORTH RUNNING QWEN FLASH? Please, please, someone, a
TL;DR My TAK quant of Qwen 3.8 27b scored 82.81% on reasoning, comparted with 77.34% for the byte matched Unsloth UD IQ2_S and 83.59% for BF16. Edit: Some of you have tried coding with this reasoning-specialized quant and encountered repetition loops. Coding is outside its intended domain, but
Since the Qwen3.5 0.8B model is an interesting one for small specialized fine tunes, I was curious how fast it can run on CPUs. Why CPUs? Mainly because I want to use it as a local dictation cleanup model when I'm using the GPU for something else. Over the weekend, I let Codex build a small C++ engi
I got 2x 20GB RTX 3080s + 128GB of DDR4 2666hz RAM (only 4 of 6 channels populated) + a Xeon 6148 I've always been a llama.cpp person and I've been running Unsloth's Q4_K_XL quant of Qwen 3.8 Flash Next at ~270tps prefill and ~13tps decode (starts off close to 20 and falls down to 13 with growing c
This weekend I posted about the gap closing between frontier models and open source models. Well, now I'm coming with receipts. I've been running local + cloud models against real public github codebases. This is all provable and verifiable: https://github.com/CYPHES-ATP/Node (audit.db) Over two we
Curious about what people are preferring, if you have the hardware. I have m3 Max 96gb and both run, and largely feel identical, but prefill on qwen 27b is faster. Is there anything / anyone working on anything to improve pp with mlx? Branching question: is anyone working on a harness that works wit
The missing control is visible in sudoingX’s Ling-3.0-flash benchmark graphics. The earlier table leaves Ling’s no-speculation baseline as “not measured.” The later code/prose graphic fills it in: about 23 tok/s without the drafter, against 40.9 on code and 38.7 on prose with MTP n=1, at short
https://preview.redd.it/qwmh23nfr3oh1.png?width=1062&format=png&auto=webp&s=4741852dc3f5e7d86ae85281b089b2a325a1804a I mean they aren't that low but seeing them much lower than Gemini-3.* flash surprises me submitted by /u/the-grand-finale [link] [comments]
Quick setup on linux: STEP 0: install/download llama.cpp, Freecad, your favourite gguf model - possibly with multimedia image reading capabilities (I've used Qwen3.8-27B-UD-Q4_K_M and relative mmproj-F16 quantized by Unsloth), uv (, pi.dev) STEP 1: $ cd /your/path/to/ (i.e. where to install) S
Hi, I use hermes as a harness, and I am pleased with it, but sometimes Hermes's context size is a bit too much for my system, so I wanted to delegate or straight up use a lighter option for coding tasks. I run a laptop with a 8gb nvidia card and 40gb of ram. And qwen3.6 35b-a3b or similar tier model
Hey all, I now have a 3090 24GB + a CMP 170HX unlocked with 64GB. I also have 64 GB DDR4. Can I run Qwen 3.8 Next in any possible configuration with both GPUs and perhaps offloading remaining stuff to DRAM? Is there a way I can get acceptable speeds? The only bottleneck with the CMP 170HX is the PCI
https://preview.redd.it/3p6234jzk2oh1.png?width=900&format=png&auto=webp&s=b9eab3351d6c6dbd4d5b3fd677a6c40b57f18167 https://huggingface.co/tencent/EVIE-8B 🌟 Highlights SOTA Retrieval Performance: 66.75 nDCG@10 on ViDoRe V3, delivering industry-leading visual document retrieval accuracy. High-
Long time user of 3.6 27b, switched over to Next Flash since it's a logical step up even from 3.8 27b. It's soooo verbose, i'm talking 13 minutes of thinking time on single turn coding requests at approximately 150 tokens per second tg and 7000 tokens per second pp. It's honestly kind of painful to
I wanted a quick calories counter for myself, using LLMs to evaluate the calories from pictures of meals + descriptions. I needed to pick a model so I made a quick benchmark. The setup was: - Nutrition5k photos for photo + calories: https://github.com/google-research-datasets/Nutrition5k - A tool wi
Was considering choosing a 4xRadeon AI Pro R9700 build for a office AI server. Curious to know what results you guys are getting? Mainly considering DeepSeek V4 flash and Qwen 3.8 Flash with offloading or heavily quantized. submitted by /u/sayamss [link] [comments]
Hello everyone. I felt current LLM benchmark harnesses hand you headline numbers but offer no tooling to see how models actually answered each question (they dump everything to a JSONL or Parquet file, so you end up writing custom code just to read the answers). So I built lm-eval-ledger: a be
I'm using DS-V4-Flash-Vision with Q8_K_XL quantization locally as my everyday engine, and for some time now I've been doing a lot of comparisons with Qwen3.8-Flash-Next, also with Q8_K_XL quantization. It took me quite a while to get Q3.8FN to work reasonably well, and here are my observations. My h
(not written by Claude, all errors and crappy text are result of too little coffee on a Sunday morning ;) Our home server is a 2018 Thinkstation P520, bought for about 600eur in 2023. It's been upgraded with a 2TB Samsung 980 Pro NVMe, a Xeon W-2145 and a 12GB 3060 - total cost about ~1k all in. N
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 what model to run given their own hardware situations. I am very interested in running a GLM model