Search: “r-localllama”

Not sure if anyone out there is working on this, don't see any on huggingface to try out. It might be too new at the moment, but it would be cool to see the difference between a tiny model trained wit

The first generation of our 150M model has just been released Its performance is similar to that of GPT2-Small The benchmarks: PIQA: 62.24% Hellaswag: 32.20% Arc-Easy: 44.91% Arc-Challenge: 25.00% Ari

A few people here mentioned interest in a way to test their custom Pi setups, so I figured I’d drop this here: RoastMyHarness The basic idea is a small engine that sets up an environment to run DeepSW

Don't upvote, just looking for CPU ram options for running qwen flash next, and maybe larger models (so maybe between 128 and 256gb total ram) with an existing cmp 170hx. Use case is agentic coding so

VLLM Benchmark: Prefill, Prompt processing - avg, 871.93 tok/s (3 hours constant running xhigh) - 10K prompt, 1000.26 tok/s (16 runs) - 90K prompt, 743,59 tok/s (16 runs) Decode, tok gen - avg, 38.39

I gave DS V4.1 Flash an HLE problem with a bash tool + 2 hours. Hour 1: it wrote three MILP solvers. (225,200) Hour 2: it downloaded the HLE dataset from Hugging Face, found the question, read the ans

It's still a dream for many folks to run medium size(30B range) models @ Q8 with Unquantized KVCache (256K Context) on their GPUs. It would be awesome to have DeepSeek-V4.1-Flash's KVCache + Engram fo

Since we know Qwen 3.8 27B thinks quite long, but gives at least a good one-shot result where you can leave it to do everything on it own, how does it compare to the older series of models for very si

It’s always bothered me that after fine-tuning a model for a project, there isn’t a particularly easy way to host it without either running it locally and keeping a GPU on 24/7 or paying for an entire

Using the RTX3090 on a linux machine I built for it and running Qwen 3.8 27B getting average 100t/s compared to my MacBook 20t/s I think I can finally get rid of my Claude subscription, this is good e

from internlm: We introduce Intern-S2-397B, our most capable multimodal foundation model for scientific intelligence and long-horizon agents. Intern-S2-397B scales along three critical dimensions: pre

I have a full model, it's ready to train. It's ~9b parameters. 9.4b to be more exact. That includes a 1/2/3 Engram table, Moonshot's AttnRes modeling, and RoPE / NoPE layering at 3:1 as more or less v

my question about LLM performance We see a lot of posts about token prediction, token generation per second, etc. But is it really the metric? I can see that DeepSeek V4 Flash 0731 (with DSPark; mac s

I'll start by saying I'm not talking about the models themselves, I'm aware that I can't come close to something like Fable's intelligence locally. Just wanted to get that out of the way. Basically. O

As a SWE I have ultimate belief that having a irreplaceable subscription on OpenAI and Anthropic models is actually against my beliefs (I'm not fond of becoming a digital slave and being unable to do

This is an evolution on top of Raymond's KV cache streaming fork - all credits to what enabled this goes to him. The basic idea behind what he enabled was a pool of memory in VRAM that is used differe

> 'make an svg of a frog playing on a chello on the back of a whale with carribean island in the back.' interestingly the svg looks different in the OpenWebUi preview then when looked at in preview (o

Lately because of the current hardware shortage, unfortunately or fortunately, we can’t just throw infinite cloud compute at our problems, but we’re forced to actually care about what’s happening unde

The entire page of the NY Times today above the fold absent one article is AI (the models are just too strong/too dangerous, must be regulated). They forgot to include 'Sponsored by OpenAI' at the end

What I have: - CPU: EPYC 7551 (32c/64T, Zen 1) - Board: Supermicro H11SSL-i (SP3), Rev 2.0 - RAM: 128 GB DDR4-2133 (all 8 channels full) - GPU: 2x RTX 3090 (48 GB total, PCIe 3.0) - 1500 W PSU What I

Applied science work, from workflow design, data pipeline, results analysis, article/reports writing and data publishing online. 5 projects I did in the past replicated from start to finish. 3x to 4x

I find this new model at HF: 'Built for demanding work. A 262 144-token context window, adjustable reasoning effort, tool calling, and text, image and video understanding. Architecture Agnes-3.0-Flash

Q2 is there and Q4 is uploading as I type. Has his github been updated yet? How do you run this? https://huggingface.co/antirez/deepseek-v4.1-flash-gguf/tree/main submitted by /u/Queasy_Asparagus69 [l

Hey everyone! I'm curious to hear from people that use a combination of cloud-based frontier models and local ones for development. I'm planning to set something similar up and wanted to hear about ac

Blog Post : Per-tensor layout maps for GGUF quantization Reddit thread : New tensor type layouts for my GGUF uploads EDIT : Model card has updated things such as Graph, table, text, etc., submitted by

I have a full M5 Pro Macbook Pro with 48GB of RAM, I'm just getting into this local space. Like many of you, the costs of using frontier/cloud models is getting kinda silly. I don't have any insane ne

So, uh... the popularity of so-called humanlike Qwen (currently on top in this sub) made me realize just how clueless the general public is about the models they have. You'd be shocked but you don't n

Kimi routed some PLA requests to Claude for distillation purposes without warning the PLA users. There is rumor that 16 Moonshot employees were arrested for this leak. submitted by /u/Ok_Warning2146 [

Hey everyone — I’m building TensorSharp, an open-source LLM inference engine. Here are the latest DeepSeek V4.1 Flash GGUF results using its native ggml_cuda backend. Setup: 8× NVIDIA A40, layer split

First off, I know that GLM, Qwen, and DeepSeek absolutely dominate in terms of SOTA Open Source models, and that’s what I use in my personal projects and for school, however, I’m also responsible for

For many, Vram is precious, I see many people recommend using --no-mmproj-offload to save gpu vram but it is painfully slow. Especially if you are using it with agentic coding. If possible, add that s

I have enabled the vision for the CIRU Strix UL4 quant of Qwen 3.8 flash next (others quants likely perform very similar) and tried it on a few things, then wanted to show my partner how great it work

https://github.com/Sayitobar/chat_llm_cookbook This is my personal project that took several months. I wanted to see whether a 2B small local model could simulate a six-person group chat trained & ran

I've seen a lot of Super Mario showcases, but never a mashup or derivative work. So I took a Super Mario generation prompt from ChopSticksPlease's Post here - and added a Matrix twist - and found myse

Another new model dropped in the course of this week that is well deployable on consumer hardware: Nex N2.5 Mini I went with the recommended settings for the best generation quality and ran a few benc

Hi! I run both models on MTPLX on my m5 max, and since I have 128GB of ram I run the q8 27b. I think MTPLX only lets me run 'optimized for speed' which it says is a dynamic q4 with 8 bit attention. Bo

Hola all. Do you guys mind sharing your LLama.cpp config and system setup details for Qwen3.8 Flash Next? Model's quite big and tryining many combinations of llama.cpp options takes lots of time, so l

As you all know, Qwen3.8 Flash Next on mainline llama.cpp is still in a pretty experimental stage, but a lot of community forks are trying to get it to work better. There's also a closed-source soluti

AuK-Flash: Fast 4-Step Speech Generation and Editing arXiv : https://arxiv.org/abs/2609.08936 Full Paper : https://arxiv.org/pdf/2609.08936 GitHub : https://github.com/Tencent-Hunyuan/AuK Project : ht

In my opinion the perfect model(s) will be around 20B-32B and A2B-A4B. This is the sweet spot on which companies should focus on. Qwen 3.8, Gemma E2B/E4B and Ling 3.0 proved it. They are still imperfe

https://huggingface.co/HermiHg/Qwen3.8-27B-DFlash2-Q2_K_S-MIX-GGUF I used this draft model with https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF with the IQ3_XXS with 128k context and I saw

I have been battling with my Qwen3.8:27b setup on my rtx 5080 16gb. I am using llama.cpp to run a nvfp4 version of qwen3.8:27b llama-b10699-bin-win-cuda-13.3-x64llama-server.exe -hf williamliao/Qwen3.

While browsing a benchmark list site, I spotted a recently published 33B parameter model which claimed to beat Qwen3.8 27b on the ArtificialAnalysis (AA) intelligence index. I was obviously excited. B

Planning to build a PC mainly for local LLMs/coding agents. I keep seeing 3090 + Qwen 3.8 Flash Next benchmarks, but could not find enough info for the 7900 XTX 24GB. 3090s are hard to find where I li

Hi! I've been following this community for quite a while and have difficulty figuring out what to put on my 3080 12gb - I know Qwen 3.6 35B 3A was the go-to choice when it first came out, but I'm curi

We have SWE-bench, Terminal-Bench, OSWorld, BrowseComp, etc. But I haven’t seen a good apples-to-apples benchmark for platforms like OpenAI Agents, Anthropic’s agent stack, AWS AgentCore, Google’s age