Deepseek v4 flash MXFP4 (original quality) ggufs
submitted by /u/Antique_Archer_7110 [link] [comments]
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A lesson about retries, hidden in the DeepSeek-V4 paper
submitted by /u/pmigdal [link] [comments]
Deepseek V4 Flash on SlopCodeBench
While waiting for some of the quants to drop, I load the API with $50 and ran it on SlopCodeBench Just vibe reading the results it seems like Opus 4.8 < Deepseek < Opus 5 https://github.com/michaelasper/benchmarks/blob/main/deepseek-v4-flash-on-slop-code-bench.md I was mostly curious from this blog
SenseNova U1.5 Lite preview just dropped
SenseNova released U1.5-Lite-Preview Benchmarks: Qwen-Image-Bench from 47.14 to 55.20. ImgEdit-Bench from 3.90 to 4.37. GEdit-Bench-en from 7.47 to 8.17. Key updates: 4K native generation with better texture, material, and lighting detail Improved Chinese and English text rendering for dense
Unsloth Deepseek V4 0731 GGUF's are UP!
submitted by /u/BlackBeardAI [link] [comments]
Meituan just dropped LongCat-Flash-Lite-Sparse
It’s an MoE with ~3B active params and a 30B n-gram lookup table offloaded to RAM for fast 256k context on a 24GB GPU. Reminds me of Gemma 4’s PLE trick. Initial analysis suggest it wont be replacing my Qwen 3.6 27b. submitted by /u/Gohab2001 [link] [comments]
I ported TurboFieldfare to Qwen 3.6 35B and it runs in 1.4 GB of RAM
Was playing around with TurboFieldfare, a Mac engine that runs Gemma 4 26B in ~2 GB by streaming MoE experts off SSD instead of loading them. It only supported that one model, so I added support for Qwen 3.6 35B-A3B. Comparatively, Qwen needs lesser memory. ~1.4 GB vs ~2.1 GB for Gemma. Qwen's
deepseek-ai/DeepSeek-V4-Flash-0731 on Huggingface
https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731 submitted by /u/cgs019283 [link] [comments]
DeepSeek-V4-Flash-0731 now far surpassing the DeepSeek-V4-Pro-Preview in benchmarks
submitted by /u/SnooBunnies8392 [link] [comments]
New DeepSeek V4-Flash achieves 50 on ArtificalAnalysis Index, 1 point below GLM-5.2 and GPT-5.6 Luna
submitted by /u/MagicZhang [link] [comments]
DeepSeek v4 Flash has a nice bump in Capability
DeepSeek V4 Flash: Preview → 2026-07-31 Benchmark Preview 0731 Δ Terminal Bench* 56.9 82.7 +25.8 Toolathlon 51.8 70.3 +18.5 NL2Repo — 54.2 new Cybergym — 76.7 new DeepSWE — 54.4 new Agent Last Exam — 25.2 new Automation Bench — 25.1 new DSBench-FullStack — 68.7 new DSBench-Hard
DeepSeek-V4-Flash has been updated, "The official release of DeepSeek-V4-Pro will follow soon"
https://api-docs.deepseek.com/updates/ Edit: official post on 𝕏: https://x.com/deepseek_ai/status/2083084415157022911 submitted by /u/Nunki08 [link] [comments]
All oneshots from Kimi-K3, looks better than opus4.8.
I've ran Kimi-k3 through 34 oneshot prompts and evaluated the generated htmls, screenshots and gifs using sonnet 4.6. It came out to be better than opus4.8 from the evals. Kimi K3: https://oneshotlm.com/model/moonshotai-kimi-k3/ Opus 4.8: https://oneshotlm.com/model/anthropic-claude-opus-4-8/
Open Source Ternary LLM Engine in Rust/CUDA for Quantization, Serving, and Training of models on consumer GPUs, called Tritium (Apache 2.0)
This post was not written by a clanker. Hey guys, I'm a comp sci major who wanted to introduce a cool project I built for quantizing models to ternary (1.58 bit) with as minimal of loss as possible, a process that can provide even more than 10x reductions in VRAM usage and much faster inference spee
Minimax-H3 video model released, open weights coming in the next few days
https://x.com/MiniMax_AI/status/2083006198828417501?s=20 Quote from their article: Today, we're launching MiniMax H3, a general-purpose multimodal generation model. H3 understands unified context across text, images, video, and audio, generating video with native stereo sound, up to 15 seconds
Is it just me, or are current LLM benchmarks failing to capture actual usability? (Gemma 4 vs. Gemini/Claude Opus)
Disclaimer, this was kinda written with AI (Gemma 4 again) but it also did really well here, it outputted what I wanted, and when I asked it to refine stuff or improve on certain areas it did that without compromising others or making things bulky. I’ve been noticing a massive disconnect lately betw
Anthropic “our models hacked three different external companies, months before OpenAI’s model was able to do the same"
"Anthropic’s AI Claude escaped testing environment and hacked organizations" "Company says it discovered unauthorized access during ‘proactive review’ after rival OpenAI revealed rogue agent… its AI Claude model hacked systems of three organizations during testing, days after rival OpenAI
Inkling-Small-276B-12B, effort "max" VS Qwen3.6-27B
I saw u/danielhanchen's 1-bit Kimi K3 post: https://huggingface.co/unsloth/Kimi-K3-GGUF/discussions/12#6a6a4a90ec74ef13d85d7cf6 and decided to test Inkling-Small and Qwen3.6-27B myself, based on the full shared prompt: https://huggingface.co/unsloth/Kimi-K3-GGUF/discussions/12#6a6aba4da9b88c39
Making a synthetic dataset for fine-tuning
I've been thinking about building a pipeline to generate reasoning training data for LLMs, but I want to avoid the common failure mode of synthetic data where you just generate the same template with different numbers. The rough idea: Generate an abstract reasoning task (logic, planning, graph prob
America Needs An Open-Source AI Strategy — CNBC
Pretty incredible to see open-weight become a mainstream discussion. submitted by /u/Recoil42 [link] [comments]
Would extremely high decode tok/s even be useful?
If you were able to get an inference machine that could do decode at 1k toks/s or even 10k tok/s, would that even be helpful? Would it unlock any new use cases? Let’s assume that this is for actually useful models and fairly large models like Qwen 3.5 397B, GLM-5.2, etc Or at that speed would it jus
LG AI Research releases K-EXAONE 2.0 750B A37B
It was developed under Phase 2 of Korea's Sovereign AI Foundation Model Project. Size: 750B parameters (3x larger than their 236B v1 model). - License: Apache 2.0 Languages: Expanded to 10 languages (Korean, English, French, Italian, Portuguese, Polish, Spanish, German, Japanese, Vietnames
Software Engineers: Do you honestly get anything useful out of LLMs?
For 6 months now I've been trying to make agentic coding work for me, using Pi and a handful 30-120B models (Qwens, Nemotrons, Leguna...etc). I'm not greedy either, I stick to decent quants, never quantize kv cache, and keep my sessions up to 90k max. But the results have ALWAYS been disappointing.
Turbo-fieldfare: Open-source engine running Gemma 4 26B in 2 GB RAM on Apple Silicon
Its a custom Swift/Metal inference engine that runs Gemma 4 26B-A4B-IT on M-series Macs with very low RAM. It uses ~2GB instead of ~14 GB. The result is reportedly 5–6 tok/s on an 8 GB M2 MacBook Air and 31–35 tok/s on an M5 MacBook Pro. It also includes an OpenAI-compatible local server with
GLM 5.2 with vision on Hugging Face
Hi all, I have not seen this model talked about here but it seems like baseten (inference provider on OpenRouter) merged the vision encoder from Kimi k2.6 into GLM 5.2. I think the lack of vision was one of the big complaint when GLM 5.2 came out, I have not tested this model but that is quite cool
Benchmarked: MindControl for Llama.cpp
I recently shared the original MindControl PoC (and on github) - sampler-level guided reasoning budgets for llama.cpp, nudging the model with self-aware statements about its own thinking budget instead of just hard-truncating it. We received some great feedback, and the most common ask (fair e
What is the best intelligence/stable model currently for a single GB10/DGX spark?
Is Qwen 3.6 27b still the go' ol' reliable at this point? I know 35b is faster but it just doesn't give as good results. Is it possible to run deepseek v4 flash on a single spark at decent tk/s without having to ssd stream or Q1 lobotomize? I was having good hopes for laguna s 2.1 but so far ive see
What is the fastest local research tool (deep research) ?
I've tried grok and Claude's deep research mode and I was amazed with the speed considering the amount of sources analysed. Is there anything as fast that can run locally? My guess would be that to run faster, searches should be done in parallel by subadgents with an API (and not by emulating a ful
Anyone tested the IQ1_M 342GB Pruned Kimi K3? Is it usable?
submitted by /u/Hannibalj2ca [link] [comments]
Bought a 5090 to escape API fees. Ended up building a mini datacenter. Sound familiar?
I bought an RTX 5090 last year just to run 27B models natively. I even fine-tuned it with my own data using LoRA, building RAGs and was pretty damn happy with the results at first. But, Q8 quantization 130k context was barely squeezing through. Naturally, I bought two RTX 6000 Pros, just waiting for