LLM Community Discusses Optimization Techniques
11 posts across 2 communities in the last 60 days; 91% from r/LocalLLaMA.
“Currently working on making it portable. It will be available through my website and possibly GitHub. It's a prototype though. The community will need test it because I can't beyond a 12b unless I succeed shrinking a 32B or 70B down.”
“Hey! Yeah please elaborate. If the hardware is a bit more expensive or not is not really a problem. So I would like to know more about it.”
“I'm the author, so discount the enthusiasm accordingly. This is an unaffiliated community port, not endorsed by the vLLM project, which it uses to verify its correctness. What started it: I love vLLM, but a vLLM install here is 9.1 GiB of virtualenv, and I wanted to embed…”
The LocalLLaMA community is actively discussing and experimenting with techniques like quantization, pruning, merging, and distillation to optimize Large Language Models (LLMs) for local deployment. This indicates a focus on efficiency and accessibility within the LLM space.
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“Currently working on making it portable. It will be available through my website and possibly GitHub. It's a prototype though. The community will need test it because I can't beyond a 12b unless I succeed shrinking a 32B or 70B down.”
Why don't you use audio.cpp instead? Much less pain and suffering
“I use https://huggingface.co/orcarouter/Qwen3.8-27B-Uncensored-MLX It’s really good”
Of course there Is: https://abliterlitics.dev/models/qwen38-27b/
“This subreddit of course, sort by newest. Huggingface directly, login and follow some of the quantizers like unsloth and bartowski”
“Hey guys I have been working on a open source LLM development repo. Anyone can train any amount of parameter against any arch (GQA, MLA, Dense...) When I tried to implement MLA I read the technical research paper on deepseek implementation and found they used FlashMLA for fast training and inference. The only thing is that it was compiled for sm_100 and sm_90 only and I couldn't find anyone that had tried to build it for consumer grade blackwell sm_120. https://github.com/IISuperluminaLII/Fl”
“Hey! Yeah please elaborate. If the hardware is a bit more expensive or not is not really a problem. So I would like to know more about it.”
“Diffusiongemma is good for some very specific things (it has obscene prefill speeds and if you use that in intelligent ways you can do monster amounts of data-processing with it, but most people haven't really used it in that way). It sucks as a general bot and you really do need 24gb vram to appreciate it.”
“Oh, absolutely. Th 9B bit is more for humor. ... but I'd love to let one go nuts in a sandbox one day.”
“I did another post about how open weight LLMs are catching up and will eventually delivery great value, they still won't be free since not everyone would be able to run them but they'll still be alot cheaper. At that point, paying a little to avoid copy paste wouldn't feel like a bad decision.”
“Hey everyone, I have published a vLLM branch which allows setting a separate sampler setting for the content which follows the <think></think> section, which significantly improves the output and reliability of Qwen 3.8 27B This branch allows setting one sampling setting for for the thinking block, and another sampling setting for everything that comes after (chat output, tool calls, etc.) Qwen 3.8 27B needs a high temperature (~0.9–1.0) while thinking or it loops. After it finishes”
“I’m curious whether there is now a theoretical or empirical “sweet spot” for LLM quantization, preferably research done using open-source formats like GGUF Suppose you have a fixed memory/compute budget and can choose the model size freely. For example, instead of a smaller model at 8-bit or 4-bit, you could fit a progressively larger model at 3-bit, 2-bit, 1.5-bit, etc. A few years ago, I remember 4-bit often being described as roughly the practical sweet spot because it preserved most model qu”
“I keep checking HF in hopes there will be a 27b variant lol. I appreciate your work 🤙”
“I'm the author, so discount the enthusiasm accordingly. This is an unaffiliated community port, not endorsed by the vLLM project, which it uses to verify its correctness. What started it: I love vLLM, but a vLLM install here is 9.1 GiB of virtualenv, and I wanted to embed inference inside other software, on machines where having an interpreter in the process is a problem. And, honestly, Python dependencies have a different deployment story, in term of security (supply chain attacks), and blo”
“one of huihui's abliterations made the model go from 50% to 65% on my alignment benchmark.. so yes i think abliteration makes them "reason better" or "removes their fear of talking truth" ?”
Independent researcher here - I thought it was clearer this way.
After awhile you will realize it is better to operate ops ex llm.
I used it to help frame my post, the post however is original
“How do you know that this isn’t a compression algo as well? Based on what I’m reading, it probably is”