Search: “model-releases”

I tried using Gemma 4 (e2b) offline to identify birds. It confidently got them wrong, so I built a photo scavenger hunt instead

I wanted to build an offline bird/plant identifier that runs on a laptop with Ollama. Before building any UI, I tested it on 6 photos: Common Myna → gemma4:e2b said 'Jackdaw' (confidence: high). gemma

model-releasesr-ollama

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Introducing EmbeddingGemma 2: A best-in-class open model for natively multimodal embeddings | Google

https://huggingface.co/google/embeddinggemma-2 EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combina

Learning to Learn a Language: in-context learning of natural language from a synthetic non-linguistic prior [R]

model-releasesr-machinelearning

Learning from data as we observe it is easy for humans, but most machine learning models have limited ability to learn from new data that they have not seen during training. Prior-fitted networks (the

Local AI ecosystem overview

Hey guys, it's Merve from Hugging Face! I've recently given a talk in a dev conference about llama.cpp + but also covering basic concepts like prefill vs decode, memory types, speculative decoding etc

Local AI World Model Part 2 - Deep NN to turn Images into Playable Characters, with prompt switching mid rollout

Last time when I posted on this subReddit to share my work, the response almost made me cry because a tiny demo got so many people talking about this In the past 2 months Ive been training a new model

Memoria 1.0.0 — a local, model-agnostic memory system for LLMs

I’ve been building this for a long fucking time, and tonight I finally released Memoria 1.0.0. I built it because I actually wanted to use it. I wanted a real memory layer for local LLM applications t

MiMo V2.6 Pro almost matched GPT-6 Astra at figuring out what a user actually wants

Introducing InferBench: A benchmark testing how well frontier LLMs infer a user's priorities from their instructions. We tested 12 LLMs across 20 scenarios with 2.8k conversations to see which models

model : add K2 Horizon dense and MoVA support by bitalov · Pull Request #29535 · ggml-org/llama.cpp

guys do you still remember K2 Horizon? https://huggingface.co/IFM/K2-Horizon-MoVA-36B-A4B-GGUF https://huggingface.co/IFM/K2-Horizon-32B-GGUF https://huggingface.co/IFM/K2-Horizon-3.7B-GGUF https://hu

Overclocking DDR5 For Faster MoE Prefill and Decode

With llama.cpp using a customized SYCL backend on Intel B70 (32GB), overclocking my DDR5 memory gave modest gains for MoE models which do not fit in VRAM. Both PP and TG increased after overclocking D

[Paper] WaveFront Decoding: Parallelized Self-Speculative Decoding for Looped Language Models

Looped language models repeatedly apply a weight-shared block to increase effective depth without increasing parameter count, but the resulting T sequential recurrent-block calls per generated token s

pi-optchat: never compact again - endless chat as a memory tree

I built a Pi extension that implements Victor Taelin's OptChat recipe: instead of compacting, every message is logged and summarized into a binary tree. Each turn starts from a fresh context with a bo

Qwen 3.8 27b just feels… ok?

I’ve seen posts here raving about how good Qwen 3.8 27b is. The benchmarks look incredible, and all the online discourse seems to deem it the best local model. I have 32GB VRAM and run Unsloth’s Q6 ve

Qwen Image 2.1 Uncensored MCP

model-releasesr-stablediffusion

https://github.com/hypersniper05/MCP-Image-Generator-Uncensored Just wanted to share with you guys my workflow converted into an MCP. It's a Qwen Image 2.1 Uncensored MCP with a few extra models I fou

Story time: Qwen3.8-Flash-Next on my Strix Halo laptop vs Claude Opus 5.5 on the same feature

For the last few weeks most of my coding has been done locally with Qwen3.8-Flash-Next, so I gave it and Opus 5.5 the same high complexity feature to build on LlamaStash (a complex and large Rust proj

SWE-Race: a coding-agent benchmark of 188 real concurrency bugs, with results from three models [P]

model-releasesr-machinelearning

We've been building a benchmark out of real concurrency bugs (race conditions, deadlocks, cancellation issues) taken from merged PRs in about 100 Python projects. Each task gets graded by the project'

Swift1.5 Qwen3.8 Flash Next - Tailored for the 96GB Mac Studio with M5 Ultra

https://huggingface.co/Dankpaws/Swift1.5-Qwen3.8-Flash-Next-MLX-4.7bpw I've had the 96GB Mac Studio with M5 Ultra for about a week now and wasn't satisfied with the results I was getting from the limi

unsloth/Qwen3.8-Flash-Next-GGUF is being updated

Looks like Unsloth is updating https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF to work with llama.cpp, so hopefully this will resolve the issue of having two different GGUF versions of Qwen Fla

Why 38% of AI Agent container escapes didn't need kernel 0-days: Analysis of 109 empirical incidents (Open Dataset + Defense Harness)

Over the past several months, we conducted an empirical post-mortem investigation into 109 autonomous AI agent security incidents (cataloged with 193 falsification criteria across tool-use and multi-a

A benchmark for LLMs playing Civilization V. GLM-5.3 is ahead of Opus-5.5, and Qwen-3.8-27B holds up surprisingly well.

A while ago, I posted here getting OSS-120B and GLM-4.6 playing full games of Civilization V. Since then, models have moved pretty far, and we wanted a better understanding about models' capabilities

Agens Volundr 32B Preview: our small team's first model on our own hybrid architecture. Only 18 of 72 layers keep a KV cache (Apache-2.0)

Hi r/LocalLLaMA. I'm on the team at Blockway, a small team in Hong Kong (disclosure: this is our model). Today we released Agens Volundr 32B Preview, the first model built on our own hybrid architectu

Finetuned 1.5B Qwen to generate bash commands at gpt-4o level using 400k synthetic examples + Fully opensource finetune dataset

Purely a hobby side project to see how far I can push a really small model, using (mostly) automated training pipelines Full synthetic data: https://huggingface.co/datasets/dirac-run/ec-training-data

Hardware Recommendations for around £6000 to £7000

After some recommendations for hardware (I'm starting from nothing), in the region of £6-7k. I will be mainly looking to use it for coding and have found Deepseek V4.1 Flash or Qwen3.8 27b good and so

How is it possible that qwen 27b is so good? When GPT 4o had a trillion parameters and was worse?

Picture from a post in r/amodei . People were praising qwen and I'm just wondering, what kind of new technologies are at play here? Does qwen just have 'better' pre training data? That's more high qua

I built an open-source real-time Japanese anime subtitle & translation engine powered by Whisper-Large-v3 + Groq / DeepSeek

Hey r/LocalLLaMA, Like many anime fans, I've always been frustrated by traditional MT engines (like Google Translate or base DeepL) when dealing with raw Japanese anime: - They completely butcher Japa

I got Qwen Flash Next Q4 running on a Mac Mini m5 64gb with ssd streaming

Bit of a side project I wanted to share. The metrics are 17.5tks decode, 360tks prompt processing based testing against my normal ai usage. I tested a couple of new things others haven’t done (at leas

I ran 1,200+ Blender modeling runs across LLMs and agent harnesses and made them votable

Blind A/B arena where AI agents build things in Blender and you vote on which result is better: https://render-arena.izolight.xyz Each agent gets a prompt that describes its environment and the rules,

Infermeld: a Linux kit for running one GGUF across AMD + NVIDIA GPUs with llama.cpp

Following on from club-5060ti and club-rdna16, I’ve put together Infermeld: a small, open-source Linux companion kit for running one GGUF across an AMD GPU and an NVIDIA GPU, powered by llama.cpp. I’m

k_llama.cpp MoE Optimizations: Expert Residency, Hybrid CPU/GPU Execution, Q2_0 Support

Finally finished my fork: https://github.com/IceFog72/ik_llama.cpp Nothing else I wanted to add/try currently works In short, it now has: Bandwidth-adaptive CPU/GPU execution Shared expert-residency i

Lessons learned while building Apex-2

Hi everyone, thank you so much for all the interest in my model. It's more than I expected. Here is a short summary of the trial and error I went through while building Apex-2. 1. GPUs were always the

MoE SSD streaming on a 64 GB Mac mini: GPU still waits 27% of decode on experts. Ideas?

Looking for some assistance /ideation. I am running qwen flash next q4 in my Mac mini m5 64gb. QFN doesn’t fit so this is done by having as many experts hot in cache as possible and streaming in the r

My qwen model hallucinated a signed URL to Alibaba cloud, normal or sketchy?

I'm using Qwen3.8-Flash-Next running on my Mac Studio as a daily driver for coding + productivity tasks, and yesterday it did something weird: I had it do some product research on amazon, so it was do

Ok how to actually learn vLLM ?

Said in title, I find the ecosystem difficult to understand, and RTFMing doesn't help me as it's never clear what is the server vs their client library ? I'm using it for voxtral 3B on one GPU, but it

Qwen3.8-27b appreciation moment

q3.8-27b q3_k_xl this thing have done everything i possibly needed from him he wired up my openwebui spawned trillium service fix all my bugs set up pi-web-ui and debug my cloudflare tunnel it even sp

Qwen3.8-Flash-Next (125B) on a single Strix Halo mini PC: 44-59 tok/s with speculative decoding, ~1,400 tok/s prefill, engine is open

Hey all. We've spent the last weeks getting Qwen3.8-Flash-Next (125B MoE, 6B active) to run properly on one AMD Strix Halo box (Ryzen AI Max+ 395, 128 GB). Tonight we're releasing both the 95 GB EXL3

Reflection AI Is About to Release a US Open-Weight Model to Take On DeepSeek and Qwen

Looks like new open model coming soon and will be 'strong' hopefully something under 200b for us memory poor. Also seeing statements about more western open models coming. Hope we get some good compet

SkyIsNotGreen/Scion-35B-A3B · Hugging Face - Ternary MoE

GitHub (llama.cpp fork) : https://github.com/sky-is-green/prism-ml-llama.cpp (Prebuilt binaries there to try instantly) Draft model : https://huggingface.co/SkyIsNotGreen/Scion-35B-A3B-mtp-drafter sub

Speakrail - a low-latency fully-local voice assistant that runs on a single RTX 4090

tl;dr: I created a fully-local open-source full-duplex voice agent that rivals GPT-Live on some benchmarks. It uses Voxtral Realtime with a turn-taking head, a microturn-finetuned Gemma 4 12B and Bree

Swift 1.5 on veloGB10, ~110 tok/s on 2× DGX Spark: xhigh beats base Flash-Next at medium on vLLM

On my two DGX Sparks, Swift 1.5 (UkisAI's reasoning-efficient fine-tune of Qwen3.8-Flash-Next) running on veloGB10 (https://github.com/sf-stav/veloGB10, sf-stav's Rust/CUDA engine built only for GB10)

Switched my local agent from Qwen3.8 27B to Ornith 1.5 35B-A3B on two 5070 Tis: about 180 tok/s vs 60, same scores on my tests

My setup is two RTX 5070 Ti 16GB cards (the second one is on an OCuLink dock) with 64GB of RAM, Ollama on Windows, and the agent runs on pi in WSL. Until last night the daily model was Qwen3.8 27B UD-

Update #5: Post training yandex/AliceAI-80B-A3B [instruct!] from scratch

Last update for those following: https://www.reddit.com/r/LocalLLaMA/comments/1wxlytt/comment/pdz728b/?screen_view_count=1 Project in a sentence: An instruct finetune of ALiceAI-80B-A3B-Base capable o

what happens to Deepseek?

I've been using deepseek for long and made valuable outputs and resources, but since the update to the 4.1, deepseek just feels dumb. I ask a simple question and process for long, doesn't plan before

model-releasesr-ollama

When Redditors come in here and ask why we run LLMs, this is why: Big AI is watching.

Anthropic Reports Florida Woman's Claude 'Diary' Threat to Law Enforcement And this time it wasn't the AI model that made the LEO referral. It was the 'human review team'. The frontier AI companies ar

Which model, which harness? I have data for you.

I keep testing harness & model setups on real tasks: basic math, vision, computer use (reading emails, navigating stores) and coding. I created a benchmark and a leaderboard. You can see it here: http

Y'all this is a sexy paper; context language models

Paper linky - Context Language Models The central idea of the paper is incredibly simple. Give a model the ability to edit its context on-the-go like a file has major benefits on task performance, con

A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems [R]

model-releasesr-machinelearning

In our #NeurIPS2026 paper “A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems (DS)” (preprint: https://arxiv.org/abs/2607.14937) we reduce a DS foundation model to

A Strata fork for IBM AC922 running Qwen3.8-FN UD-Q4_K_XL is doing up to 7,357 tk/s prefill and 113 tk/s decode

I forked Strata and worked with Opus 5.5 with some heavy changes to it to make it work on an IBM AC922 I have access to. The IBM AC922 is a 2018 era beast with two POWER9 20 core SMT4 CPUs that are co

Built a quick, sub-15ms Rust CLI/TUI to pack repos into prompts without burning 40k tokens on lockfiles and junk

Whenever I feed codebases into local models (Qwen, DeepSeek R1) or API models, the biggest annoyance is prompt pollution: - Lockfiles (`Cargo.lock`, `package-lock.json`) burning 30,000+ tokens for zer

[Discussion] A 5KB pure x86-64 assembly engine for Gemma-2B (FP16, 4.6 tok/s on CPU)

Hi everyone, Sharing a personal project exploring the minimal bare-metal footprint required to run an autoregressive LLM. Instead of relying on large runtimes or compiler abstractions, I wrote an infe