独立 AI 资讯库
从自动抓取数据库实时读取，适合快速浏览、检索和后续扩展成完整资讯站。
I've been building whatmodel.sh, an open source AI leaderboard, and wanted to see how a labs' own benchmark scores compare with independent testers. Using 1,629 published benchmark results, I matched 611 comparisons of 180 models on 103 benchmarks. AI labs reported scores 6.4% higher on averag
hey, my name is Ye, I vibecode at Nace.ai and we do a lot of work with enterprises, and a lot of the time we're inside their networks with no internet access and outdated cloud hosted models. considering we work on macbooks, we have built Kapitsa, an open-source (Apache 2.0) inference engine f
I maintain TensorSharp, an open-source local inference engine and agent runtime. I’ve added Video Studio to bring local video generation, an editable timeline, and an optional local LLM assistant into the same project. The attached demo runs in TensorAgent on macOS. The actual run Machine: Ma
DeepSeek-V4.1-Flash-1017's Terminal bench 4.0 score: - Model card reported 42.3% - AA reports 34% - Vals index reports 31.82% AA Vals Index Model card Model card quotes: We use the Claude Code harness for Terminal-Bench 4.0 I am guessing AA & Vals index uses mini-swe-agent thus causing a low
Hi folks! We are HardenedLinux, an old-school free software community. SonicBoom is our open-source (GPLv3) inference runtime: a compiler-first alternative to the usual Python stack. No Python at build or run time — one shared library, a C ABI, and Guile Scheme as the first-class language. What v0.0
I've been working on optimizing Qwen 3.8 Flash Next on my local setup with two CMP 170HX cards and finally got to a place where I feel like the results are worth sharing. Both cards are running over PCIe Gen2 x4 - no x16 mod or TP2. The setup uses the W4A16 AutoRound checkpoint, a patched vLLM, pip
Another little challenge I seen on a DeepSeek Thread. I thought I'd try out it out on Qwen FN NvFp4. It was a pretty straight forward prompt with 4 images attached in the doc. The thread was testing different harness and models and showing their results. Op of that thread has two other threads
Google just released Harness2 as open source. It turns any AI harness into a system that improves itself over time. A harness is everything around the model. System prompt, tools, memory, skills, and subagents. Normally a person designs it once and the agent uses that fixed setup for every new
Seems to speed up inference, use less tokens, and increase intelligence - curious if people seen this tldr don’t think in text, think in latent space https://github.com/togethercomputer/HybridAgent https://huggingface.co/collections/togethercomputer/hybridagent submitted by /u/corruptbytes [
What a week. Quick recap, all numbers from the official repos and model cards: Thursday: Meta drops Muse Spark Open. 816B total, 42B active, 1M context. The launch post says it brings agentic AI "to your desktop". The desktop is two M5 Ultra Mac Studios with 512GB each, a config Apple says shi
Curious what everyone runs in practice: - What quant do you use most, and for what (chat, coding, agents)? - 2-bit: do you use it? Is it actually usable or too broken? - 3-bit: good enough for coding and more complex stuff, or do you go back to 4-bit? - If you look at KLD: what number do you call "b
i wanted to change my router firmware to openwrt and so i told claude to do it but of course the guard blocked me even opus 4.8 didn't want to do it , so i launched my Qwen3.8 27b and told it the same thing i gave it the link to the giude page and it did change the firmware perfectly the thing is th
With winter coming up where I'm at, I wanted to see if this could actually work. Running my 4x V620 server flat out made my apartment too hot anyway, so the problem really was control. I settled on using a motherboard temperature sensor with an offset cause cases get hot. It works well enough but I
Qwen4s are hopefully coming pre-Xmas… there was all the announcements recently but no exact release date. I’m running qwen flash next 3.8 at about 28tokens per second and gotta be real, most of those go into thinking. Thinking less with the same or higher intelligence would be the biggest upgrade I
Hey y'all, just following up on my PR to get the new vision tower for 1017 working in ds4 with the quant I promised (the language model loads already, but vision needed my PR). I built to the exact same recipe antirez used for his original DS4.1 Q2: 2-bit routed experts with 8-bit or higher fo
I accidentaly bought 3 rtx8000 48gb (yeah, i swear). I am using 1 right now with Strata and Qwen Next IQ3 and runs very good. Should I put 3 on a PC and runb Strata on a higher Q? What do you suggest? Thanks! submitted by /u/evillarreal86 [link] [comments]
Been seeing a lot of people with their 64gb of ddr5 4x3090 or whatever strata builds and it’s been sorta depressing knowing i cant afford that but i finally finished my setup today so here we go, Qwen 3.8 27b at 262k context, unsloth iq4XS ascii condensed with kv cache streaming My only gpu is a 906
Instead of counting 'hmm' on DeepSeek v4.1 Flash 1017, I today clamped Muse Spark Open 816B-A42B to ds4. This was the first successful run, FTW, that made it to decode... 4 t/s! #RoastMe. I'm hoping it will 4-5x with any luck. I'll obvs be running larger prefill chunks too (pre-metal leftover
**TL;DR:** DeepSeek-V4.1-Flash-1017 (EXL3 2.9bpw, 1M context) on 2x DGX Spark, TP=2 over the CX7 cable. TensorFold is 2.1-2.3x faster than the vLLM kit on the same pair, same weights, same prompts, same day. vLLM kit TensorFold Decode, text 27.0 tok/s **56.8 tok/s** Decode, code 39.9 tok/s *
On my previous posts, people argued these thinking rules might work on short tests but would hurt long-horizon tasks. So I tested them on Terminal-Bench 2.0: same model (GLM-5.3-Flash), same agent, every task run with and without the rules. I ran it on High thinking as Max thinking was taking
Basalt: Qwen 3.8 FN NVFP4, single 5090 Hi guys, so I finally got a RAM upgrade and have been working on the top two pain points from Basalt: NVFP4 support and faster prefill. NVFP4: Basalt runs NVFP4 natively now, a new .basalt pack keeps stock NVIDIA experts and stores the dense layers as Q6_
Hey all, been meaning to post this for a while. I've been building an inference engine based off Strata called Xolotl that runs DeepSeek-V4.1-Flash, all 552B of it, on one gaming PC. My setup is a single RTX 4090 (24GB), a Ryzen 7 5700X, 90GB of DDR4 and an NVMe drive, nothing fancy. The weights get
DISCLAIMER: the prefill rate on the dashboard in the video counts cached prompt tokens as if they were read again, so it reads high. The captions show the server’s real end-to-end numbers, and so does everything below. The decode numbers on screen are correct. After the last post I wanted to t
Hi all, as many of you are probably aware DeepSeek V4.1 Flash 1017 was released yesterday. I put up an abliterated model + GGUF quants for both the base and abliterated version (no imatrix yet that's WIP) For the abliterated model, most refusals are successfully removed. On the original V4.1 m
The new models like Qwen3.8 27B and DeepSeek V4 Flash etc., all have this terrible, Claude-sounding writing style that I can't stand. Here's an example of what I'm talking about: "A probe that asks what a language model memorised reports a null when the model answers at chance, and that null is evid
Total Parameters 4B Architecture Looped (latent-recurrent) normalized-GPT with interleaved sliding-window attention (SWA) and full-attention layers Precision BF16 Context Length Up to 256K tokens Supported Languages English Best For General-purpose reasoning on a small footprint License OpenMD
submitted by /u/Informal-Trouble2183 [link] [comments]
Already posted here https://www.reddit.com/r/LocalLLaMA/comments/1x85hfj/strata_qwen_38_flash_next_fast_but_whats_it/phavui9/ , but I believe this topic deserves it's own post. The PLE, aka the n-gram table that sits on the SSD, is quantized heavily in the common GGUFs and has a measurable impact o
I have been working on implementing adaptive KV streaming and RDNA 4 / ROCm optimizations for llama.cpp to enable running Qwen3.8-27B (IQ4_XS weights, Q8_0/Q4_0 KV cache) with its full 262,144-token context window on a single 16 GB RX 9070 XT GPU at 80+ t/s decode. Qwen 27B IQ4_XS is ~14.3 GB,
Following a recent interesting post that challenges the performance of higher vs lower quants and generally advised sampling parameters (namely temp), I decided that I had too much free time on my hands and ran 12 test tasks. I ran three models that work on my hardware: - q3_xxs - q3_s - q4_xs