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LocalLLaMA
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Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
Get support from the community! Ask questions, share prompts, discuss benchmarks, get hyped at the latest and greatest model releases! Enjoy talking about our awesome hobby.
As ambassadors of the self-hosting machine learning community, we strive to support each other and share our enthusiasm in a positive constructive way.
Rules:
Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.
Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.
Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.
founded 2 years ago
MODERATORS
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NousResearch is quietly cooking some fascinating stuff presumably for full release of DeepHermes!
(lemmy.world)
Something I always liked about NousResearch is how they seemingly try to understand cognition in a more philosophical/metaphysically symbolic way and aren't afraid to let you know it. I think their unique view may allow them to find some new perspectives that allow for advancement in the field. Check out AscensionMaze in particular the wording they use is just fascinating.
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It's amazing how far open source LLMs have come.
Qwen3-32b recreated the Windows95 Starfield screensaver as a web app with the bonus feature to enable "warp drive" on click. This was generated with reasoning disabled (/no_think) using a 4-bit quant running locally on a 4090.
Here's the result: https://codepen.io/mekelef486/pen/xbbWGpX
Model: Qwen3-32B-Q4_K_M.gguf (Unsloth quant)
Llama.cpp Server Docker Config:
docker run \
-p 8080:8080 \
-v /path/to/models:/models \
--name llama-cpp-qwen3-32b \
--gpus all \
ghcr.io/ggerganov/llama.cpp:server-cuda \
-m /models/qwen3-32b-q4_k_m.gguf \
--host 0.0.0.0 --port 8080 \
--n-gpu-layers 65 \
--ctx-size 13000 \
--temp 0.7 \
--top-p 0.8 \
--top-k 20 \
--min-p 0
System Prompt:
You are a helpful expert and aid. Communicate clearly and succinctly. Avoid emojis.
User Prompt:
Create a simple web app that uses javascript to visualize a simple starfield, where the user is racing forward through the stars from a first person point of view like in the old Microsoft screensaver. Stars must be uniformly distributed. Clicking inside the window enables "warp speed" mode, where the visualization speeds up and star trails are added. The app must be fully contained in a single HTML file. /no_think
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This is one of the "smartest" models you can fit on a 24GB GPU now, with no offloading and very little quantization loss. It feels big and insightful, like a better (albeit dry) Llama 3.3 70B with thinking, and with more STEM world knowledge than QwQ 32B, but comfortably fits thanks the new exl3 quantization!
You need to use a backend that support exl3, like (at the moment) text-gen-web-ui or (soon) TabbyAPI.
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Microsoft researchers build 1-bit AI LLM with 2B parameters — model small enough to run on some CPUs
(www.tomshardware.com)
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