Search: “model-releases”

There's an interactive chart and some extra data in the blog post if you're interested. There are plenty of KL-divergence benchmarks for GGUF models, but most of them compare one GGUF quant against an

A few months ago, I had the idea of making a LLM from scratch as a personal project (for learning and partly for improving my resume). Since I learned a lot from other posts on here over the past year

Looks like the Ling team open weighted a much smaller version of the Ling-3.0-flash they open weighted a few days ago. It's 8B params with 1.3B active, and seems to fall between the 4B and 8-12B Qwen

Hi r/LocalLLaMA 👋 Today we’re excited to release Muse Glimmer, a 30B open-weight model built specifically for local agent workflows. We’re releasing the weights to the community under a permissive Apa

If you read -> huge claims + AI like generated presentation + no negative comment AND '$10 to download on my patreon/whatever' = they're scammers. Period. 1- Anyone leaving a negative comment or tiny

Motif-Technologies is one of the tech company participated South Korea's AI Foundation Model project.(독파모) Upstage(Solar Series), LG AI Research(EXAONE Series), and SKT(A.X Series) are the competitors

I did some testing this morning, and I was surprised to find that Muse Glimmer actually comfortably fits on a single RTX 3090 with full context + DFlash + mmproj at Q4_K_XL, unlike Qwen3.6-27B and Gem

Hey. Just tried it on my old ass gpus 😄 Surprisingly Tensor Split is working on 2 gpus almost doubling PP (wonder how it will work with 4 gpus) Q6 — 1 GPU llama-server --model <MODEL_DIR>/Muse-Glimmer

I've been building a personal project and wanted to check with the community on multi-modal inputs since I can't find a lot of material around this online. Ultimately I'm trying to build something tha

I'm a retired platform engineer, coding mainly in Rust, and involved with a ML study group. I developed a ML programming language (alternative to Python, Colab) to help me learn (and teach) ML concept

Hey LocalLlaMa, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots a

Prompt: 'integrated_multimodal_description: [Shot 1] Live-action, ultra-realistic first-person footage at night on a rainy city street, filmed with authentic handheld smartphone qualities. The phone i

I have a classic test for local LLM's. I asked for 8 ball pool game with only one HTML file and Muse Glimmer spend 21k Token(I m using full context so 128k) and only created a 220 lines of HTML and sa

I also have 64 gb ddr4 ryzen 5600 Using llama.cpp Ubuntu distro Settings are as follows --n-gpu-layers 999 --n-cpu-moe 37 --no-mmap -ctk q8_0 -ctv q8_0 -fa 1 -c 9000 submitted by /u/Sweaty_Perception6

Summary from https://www.reddit.com/r/StableDiffusion/comments/1vh9rtw/ama_minimax_h3_team_ask_us_anything_about_our/ This summary was compiled with AI but cross-checked manually by me for accuracy. I

Ran the model with quants (Q4) by Unsloth with latest (build from master) llama.cpp server. It takes ~20GB ram running on M5 Pro with 48GB at about 17t/s. Didn't do any reasoning loops/overthinking. O

I have been trying to parse word docs for use with llama3.1:8b in Ollama. I only need the text - Even if I cut and past into a text editor weird characters seem to stick around which break llama/Ollam

I kept seeing model file size compared directly with the number printed on the GPU box. That misses several memory buckets. A simple planning model is: usable capacity = advertised VRAM x 0.90 total t

Demand for low-precision inference, including NVFP4-based approaches, has grown as large language models are increasingly deployed in latency and cost constrained production environments. Quantization

Available context length with and without the patch: Model: QWEN 27B ROCm stock patched Vulkan stock patched IQ4_XS Pure, single 16GB GPU 19.456 76.032 68,352 78,592 Q6_K_L on 16GB + 12GB 64,256 149,2

What Local Embedding + Reranking Models are you guys running for RAG? I went down this rabbit hole because I wanted a Embedding Model + Reranker for a Translation Memory Server. Essentially, given X p

Hey everyone ! Quick share from the cyber + local LLM side of things that I found interesting. During this week’s hacker summer camp, an AI researcher and reverse malware engineer veteran 'lordx64' on

Disclosure: I’m the author of Ante. DeepSeek recently reported an 82.7% score on Terminal-Bench 2.1 for DeepSeek V4 Flash 0731. Its evaluation used “DeepSeek Harness minimal mode,” which hasn’t been r

After seeing the benchmark results for the full release of DS v4 Flash 0731, I replaced my 2 x 16GB DDR4 ram sticks with 2 x 32GB DDR4 ram sticks to get a max supported of 128 GB RAM, in hope to be ab

Hey everyone, I've been experimenting with the new DeepSeek-V4-Flash-0731 release locally using the Unsloth Studio Q8_K_XL GGUF with OpenCode. Overall, it's been working really well, but I've noticed

Am I the only one having issues with DeepSeek V4 Flash? It gets stuck in a loop, as if it can't call the tools, and keeps repeating the same things endlessly without moving forward. Is it a poorly wri

table bench https://huggingface.co/bartowski/endless-frontier_BigBang-v1-GGUF I'm downloading this model only because Bartowski converted it to .gguf, so it might be interesting. Doubts : The headline

First of all, I'm not a lab, this was a solo summer research project that finally culminated into the github repo and the writeup. The repo includes a much deeper dive with methods, findings about qua

Hey everyone, I constantly see high praise for M3 and M4 Macs for local LLM inference, even the base/16GB models. However, my experience has been quite different, and I'm trying to figure out if I hav

I have a Xeon w7-3465 and 4 sticks of RDIMM DDR5-4800 with a theoretical max bandwidth of 153GB/s. I am trying to run DeepSeek-V4-Flash-0731 as it is an MoE and the weights are in MXFP4, so I should r

*SupraLabs released a new experimental model!* SupraElegans-500K is a ~500,000-parameter causal language model built around a sparse, signed, recurrent neural graph. No Transformer, no attention mecha

Tweet by u/hackerllama Could be copium, but I would love to see Gemma 4.1 there with unified audio input for all model sizes perhaps even up to 120B, much improved tool calling (even with the latest t

There are so many posts where people complaining about high prices and asking for solution <= 1000 EUR. So, there is one solution to consider: PC/mini PC/laptop on Ryzen 7 260/Ryzen 9 8945HX/etc CPU w

Howdy - I posted a benchmark here - https://www.reddit.com/r/LocalLLaMA/comments/1vbtiy7/deepseek_v4_flash_on_slopcodebench/ This was using the hosted API - since then I've been playing around with qu

I have various quants of this model and am curious how they perform. can anyone recommend which benchmark would be a good test case for quantization effects? Maybe that can be completed with about 1 m

I’ve been using 3.6 27B Q4, and that quant is fast on an M5. The code has been average, but consistently “good enough.” And, after a year, I can see home LLMs being served at home much like streaming

I’m planning a dedicated home AI server, mainly for local LLM inference, agents/tool use, Docker services, and eventually larger MoE models with CPU offload. My plan is to start with 2x 16GB GPUs = 32

I was wondering what a minimal coding agent implementation would look like that can be used like Claude Code or Codex Not feature-by-feature of course but basically stripping everything out that is no

I’m running DSV4F 0731 on dual spark, and honestly… wow. It’s an absolute workhorse, and the benchmarks are real. Everyday tasks with Hermes agent? Effortless. Coding tasks with OpenCode? I’m genuinel

Disclaimer - no LLM was used to write this post/note As larger post about my setup will come later, want to give heads-up to folks who use VLLM and >= 2 GPUs. So I have pretty meaty server (8 channel

Hey everyone, I could use some advice on setting up speculative decoding correctly with llama-server. My Hardware: GPUs: RTX 4090 + RTX 6000 Pro (120GB total VRAM) RAM: 32GB I am currently testing the

I’m building DesktopLab, an open-source local-first control plane for development agents. I recorded the setup boundary that most agent demos skip: DesktopLab detects the host, proposes the supported

(I am not a native speaker, written by myself, so please bear with me) I really want to like DeepSeek-V4-Flash-0731. But it has serious flaws that don't align with the high score on intelligence bench

Firstly a big thanks to the poster 'hellohazine', he basically only removed the multi-lingual fat of the model and just kept the English language intact. It is the exact model, and the rest of the mod

This PR should be ready for testing now. I tested with a very small (8B params) sub-model extracted from the original one. Appreciate if someone can test with the bigger model. GGUF(for testing) from

Running across 2 clusters using llama.cpp over RPC too. Both clusters are not enough to hold everything in memory, so main cluster still partially offloads to run. Goal will be to get all the GPUs in