Knowledge catalogue
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
last night I was messing around with Codex and threw in a stupid prompt: **“hey codex, find me a girlfriend. make no mistake”** I expected the usual AI answer. You know, go outside, join a hobby club,
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
We’ve been watching both models closely on InferX, and something interesting is starting to show up. GLM-5.3 Flash might actually be outperforming DeepSeek V4.1 Flash for some real-world workloads. To
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
Same 44-token prompt: low = 202 output tokens in 15s, max = 4,955 in 35s, unset = 10,579 in 68s. I'm using it via claude code with this: CLAUDE_CODE_MAX_CONTEXT_TOKENS=1048576 ollama launch claude --m
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
I tried GLM flash a few weeks ago and expected the token weekly limits to be comparable to DS flash. For some reason, it was a huge use of my limits. Am I the only one with this issue? I did send a su
I’m genuinely mind-blown by what’s happening with AI models right now. On one side, you have GPT-6-class frontier models that are just… insane. The level of capability is getting hard to wrap your hea
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
Tested on Laptop with RTX 5060 8gb vram and 16gb ram Claude code - autocompact = false Ollama - Flash attention = 1, kv cache = q8_0 Gemma4 12B 64K - https://ollama.com/SetneufPT/Gemma4-12B-IT-QAT_Q4_
Hi everyone, I was interested in learning LoRA fine-tuning, and ended up building CodeFinetuner over the past few months, a full pipeline that fine-tunes a small code autocomplete model (e.g. Qwen2.5-
Hey everyone, If you run local models via Ollama in production or personal projects, you've probably run into the hallucination problem: how do you know when a model is hallucinating without burning e
I wanted to see how my fully local home voice assistant compared to the latest GPT Live, so I tested it using the same conversation used in their 'Improved Intelligence' demo. In this video they ask t
A huge favor to ask llama maintainers - please implement this feature. Even with one 3090 card there will be tangible improvements in decode speed on MOE models with moderate number of active paramete
AI-generated because I’m currently way too high to write this coherently. I have HPPD (Hallucinogen Persisting Perception Disorder), which basically means visual effects from past psychedelic use can
Hello Guys, Just sharing this harness I've created, this runs entirely on chromium based browser. No installation is required, it has its limitations and by no means is supposed to replace better agen
I just watched a YouTube from Luke’s Dev Lab where he literally just plugged a RTX 2000 ADA Into the side of the Zima Board 2’s PCIE socket and it just friggin worked and had great token speed despite
Hey gang, im wondering if you in theory could use ngrams as seen with Qwen 3.8 Flash or DS4.1 in order to dynamically train the model? Normally the ngram embeddings behave similar to a lookup table of
I've been out of the loop for some time. Is there already an official way to offload ngram to RAM or SSD in something like Unsloth Studio? Interested in running Qwen3.8-Flash-Next on 72GB VRAM, but na
I haven't seen this mentioned yet, so I thought it deserves a post. I was trying out OpenWhispr when this model came up as the recommendation. So I don't have personal experience yet, but it's suppose
I made this because I was getting genuinely annoyed at trying to have a normal conversation with LLMs. Even with prompting and various tricks, most models I've tried still have this 'AI assistant' vib
I can't stand kv cache quantization. Even at q8_0, I can feel the difference. But realistically, when running Qwen3.8-27B-UD-Q4_K_XL on my 32 GB GPU, I only have room for ~170k tokens (with mtp and mm
I’ve been building 'Spomin', a router that replaces context with summaries directly in the KV cache. The goal is to keep long running sessions going without repeatedly stopping for full compaction and
Some people says terminal bench reflects model intelligence better than the intelligent index. From the look of it, the ranking does seem to reflect how people feel about the open and closed models. F
That's amazing. I've been using the pay-as-you-go plan, and because DeepSeek 4 Flash wasn't great, I was using GLM 5.3 Flash instead. But now that I'm using the high-performing DeepSeek 4.1 Flash, the
Astra's launch has produced a strange discourse. The reporting that broke the story framed the model's use of recurrent depth primarily as a safety regression, because it means the model reveals less
i've been thinking about this more after trying different agent setups. the model isn't the only thing that determines how well an agent performs. The harness around the model matters a lot too. With
I wish I could spend $15k on my own homelab hardware, but no this is for work lol. Like the title says, we're looking to run DSV4 Flash (and similar tier models) locally at good speeds, both for token
Looking for tools to build/run an engine to do STT-LLM-TTS basically. I know how to run all of them separately, and if need be I can build one myself, but I was curious if there is already an open sou
I'm currently building my system around 3060s, but I might be able to get a 4060 for a nice deal. At first it seemed like a no brainer, but turns out the 4060 has lower memory bandwidth. In a system t
Spoke with many providers who lurk in this sub, plus met folks who work in inference. For a 10k monthly revenue, a provider was able to retain only 200 dollars in profit due to GPU costs. Their custom
Like many of you, I have seen many posts and tweets in the last weeks complaining about Artificial Analysis being 'broken', 'meaningless', and 'bought out.' People who say this have done no research a
OpenAI has decided to fully shut down a protein design project I'm working on for a client. Needless to say, open weight models are the only way forward. submitted by /u/Terminator857 [link] [comments
Title says it all. https://github.com/gjabdelnoor/Day1DeepseekV4.1-CPU The goal is pretty simple, I like having infinite slow tokens from the bioinformatics machine in the lab to run overnight or over
The downside of uncensoring a model is that it is known to potentially damage it, but CyberTiel is an even more capable software engineer than its censored TielCoder base, while allowing offensive sec
Here we go again, DeepSeek is back again with a new model V4-1 Flash A multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for contexts of up to one million tokens Mark
People keep on getting confused about this, so I looked at the safetensors on hf. To be precise, the main model about 551.566B parameters with 40 layers. The FFN experts total to 543.582B parameters,
I like to benchmark new models that come out on motion videos. So here's a test I did for deepseek v4.1 flash. And I have to say flash has probably graduated from being a Luna class model to nearly an
Original Source from DeepSeek WeChat Official Account: https://mp.weixin.qq.com/s/qg0NU3NNUbp1co2PdkAPAg Today we're officially releasing the DeepSeek V4.1 Flash model. It is the smallest model in our
I believe I may currently hold the record for memory constrained inference for Qwen3.8–Flash-Next on Apple Silicon — needing only about 21GB of allocations. Introducing Cherenkov, an inference engine
Hey y'all! We've released a new model in our lineup: GigaChat-3.5 Reasoning. It's a 432B-A28B MoE with Gated DeltaNet for long-context efficiency. We trained domain experts (code, math, general, etc.)
https://preview.redd.it/4c6otbeg1qoh1.png?width=1139&format=png&auto=webp&s=2d127bd3bc83d5e6ba65c3a4bd909529f34e9a3c Total beginner here, i've just downloaded Ollama and Gemma4:26b, but when it answer
So I was playingw ith embedding models I saw that when you upgrade from model A to B, you face a very big backfilling cost Ie, suppose you have a 1b vectors from model A, and then you want to use mode
First off, I'm super thankful for the value Ollama has given me Got me setup with my first local LLM setup (early OpenClaw days amirite!?) Then got me into their 20 plan and what really sold me here w
I’ve been working on perfecting my setup ever since 3.8 hit, but no longer pay for subscriptions. I had a coding interview today and setup openrouter ahead of time as another option. I figured I could
After noticing that it is ranked among MUCH larger frontier models in the EQ-Bench Creative Writing benchmark and the Hemingway-bench, I decided to give it a try and was very impressed. I didn't do ve
Spent about two weeks convinced I had a prompting problem. My local Qwen3 agent would just stop. No error, no crash, finish_reason came back as 'stop' like the model had decided it was done. Except it
are very strict... if 20 calls to kimi are more than my 5 hour limit why even bother? im gonna cancel my sub for sure https://preview.redd.it/gbqkci93gnoh1.png?width=1080&format=png&auto=webp&s=6fac5a
Both good and bad things have come from a subreddit that was lot more niche than for example r/flashlight rapidly transforming into the largest online forum about an increasingly core part of the infr
so i saw that openui.com released OUI-1, a model fine-tuned on DiffusionGemma. the training dataset uses OpenUI-Lang, a custom DSL (domain-specific language), instead of plain HTML, Markdown, or React
Github Repo. Blog post. 💡 TL;DR (from the Github Readme) Spend less without making the agent do less useful work. SoL-Pi is a standalone extension for Pi that packages four reusable efficiency mechani