Recent submissions

Comment · 2 months ago
I've wanted to write this one down for a while, because it's been fun so far and I want other people to experience it. Apologies for the length, hope it interesting either way.
Comment · 3 months ago
It's pretty simple nowadays if you know conceptually how they work. Running the LLM calls in a loop with tools is an agent. You only need 10 or so basic tools to accomplish nearly anything, and you can build a dynamic skill system from that. Look at https://github.com/patw/pengy, ignore the app look at the spec.md file, feed that to your current agent of choice and make your own version. Use whatever tech stack or UI you're comfortable with. Change some of the choices in how it works, so it fits what you want to work.
Comment · 6 months ago
You might have a business idea there. I wouldn't mind a twinscan plushie for sitting on top of the workstation.
Comment · 1 year ago
Yeah 48g, sub 200W seems like a sweet spot for a single card setup. Then you can stack as deep as you want to get the size of model you want for whatever you want to pay for the power bill.
Comment · 1 year ago
Sure, all the slop code projects I produce get MIT licensed on public repos. It wasn't mine to begin with, so I wouldn't prevent anyone from using it.
Comment · 1 year ago
Used 3090s have been getting expensive in some markets. Another option is dual 5060ti 16 gig. Mine are lower powered, single 8 pin power, so they max out around 180W. With that I'm getting 80t/s on the new qwen 3 30b a3b models, and around 21t/s on Gemma 27b with vision. Cheap and cheerful setup if you can find the cards at MSRP.
Comment · 1 year ago
About 768 gigs of ddr5 RAM in a dual socket server board with 12 channel memory and an extra 16 gig or better GPU for prompt processing. It's a few grand just to run this thing at 8-10 tokens/s
Comment · 1 year ago
These articles are gold, thank you. I used your gemma one from a few weeks back to get gemma 3 performing properly. I know you guys are all GPU but do you do any testing on CPU/GPU mixes? I'd like to see the pp and t/s on pure 12 channel epyc and the same with using a 24 gig gpu to accelerate the pp.
Comment · 1 year ago
I run the KV cache at Q8 even on that model. Is it not working well for you?
Comment · 1 year ago
Qwen is a little fussy about the sampler settings, but it does run well quantized. If you were getting infinite repetition loops, try dropping the top_p a bit. I think qwen likes lower temps too
Comment · 1 year ago
You probably won't be running fp16 anything locally. We typically run Q5 or Q6 quants to maximize the size of the model and context length we can run with the VRAM we have available. The quality loss is negligable at Q6.
Comment · 1 year ago
Comment · 1 year ago
Comment · 1 year ago
It's a 3b model so the creativity is pretty limited. What helped for me was prompting for specific stories in specific styles. I have a python script that randomizes the prompt and the writing style, including asking for specific author styles.

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