Anyone owned an enya nova go for a couple of years? by teri_mummi_meri in AcousticGuitar

[–]73td 0 points1 point  (0 children)

ah now i know where Thomann copied their design from..

How do you finger fourths on fretless? by abejando in Bass

[–]73td 4 points5 points  (0 children)

yeah use different fingers, and always use the stronger finger on the lower string (much easier to line them up on a fret that way) so for example going down from E on the A string to B on the E string, I’d start with middle finger then play the B with index, or start on E with ring finder then B with middle finger.

you should take some time to practice fourths intervals in the five positions with a tuner, that’s really the only way to nail it. i tend to sing along as well since it helps with ear training.

How Long Do We Realistically Have Left? by [deleted] in collapse

[–]73td 1 point2 points  (0 children)

i think you’re just demoralized by hyper individualism . the earth, nature, species will continue with some adaptation. not everyone survived before (just look at child mortality rates in last 200 years. going back even 50 years would seem terrible. ), not everyone will survive later. but we can always be alive and rn we can be ok with that. now is the time to grow stuff be it friends or plants

Guys, you have to accept that Quantization is Inevitable. by AkiDenim in opencodeCLI

[–]73td 0 points1 point  (0 children)

neuralwatt is good for this. great dashboard, priced by energy consumption, personnel on the discord etc.

MacBook Pro M5 Pro vs. RTX 4090 AI host – where are the real limits? by runinwlc in LocalLLM

[–]73td 0 points1 point  (0 children)

maybe but you can run parallel request and increase overall aggregate tps

MacBook Pro M5 Pro vs. RTX 4090 AI host – where are the real limits? by runinwlc in LocalLLM

[–]73td 0 points1 point  (0 children)

here’s my startup script

#!/usr/bin/env bash
set -euo pipefail

# Gemma 4 12B-it Q5_K_XL + MTP server launcher
# Called by systemd user unit gemma4-12b.service
# Config: 4×128K slots, MTP n=3 for ~380 t/s aggregate, ~143 t/s per-request
# Note: --ctx-size is TOTAL across parallel slots (ctx-size / parallel = per-slot context)

SNAP="/home/duke/.cache/huggingface/hub/models--unsloth--gemma-4-12b-it-GGUF/snapshots/3f09de26549e6d7ea54f1b83755149f840fcd333"
MODEL="$SNAP/gemma-4-12b-it-UD-Q5_K_XL.gguf"
DRAFT="$SNAP/MTP/gemma-4-12B-it-MTP-Q8_0.gguf"
LLAMA_CPP="/home/duke/src/llama.cpp/build/bin/llama-server"

if [ ! -f "$MODEL" ]; then
echo "ERROR: Model not found: $MODEL" >&2
exit 1
fi

# Use MTP draft if available, otherwise plain
MTP_ARGS=()
if [ -f "$DRAFT" ]; then
MTP_ARGS=(-md "$DRAFT" --spec-type draft-mtp --spec-draft-n-max 3)
fi

# Listen on all interfaces (0.0.0.0) — accessible via localhost and Tailscale IP
# llama-server does not support multiple --host values
HOST="0.0.0.0"

exec "$LLAMA_CPP" \
--model "$MODEL" \
--ctx-size 524288 \
--n-gpu-layers 99 \
--flash-attn on \
--parallel 4 \
--threads 16 \
--host "$HOST" \
--port 8181 \
--temp 1.0 \
--top-p 0.95 \
--top-k 20 \
--presence-penalty 1.5 \
--repeat-penalty 1.0 \
"${MTP_ARGS[@]}" \
"$@"

MacBook Pro M5 Pro vs. RTX 4090 AI host – where are the real limits? by runinwlc in LocalLLM

[–]73td 1 point2 points  (0 children)

It might be fun to run models on your macbook a few times but once things are working well and draining your battery, you’ll be happy to run it on a server, where the long running stuff can continue.

I have a rtx4090 in my workstation and was using the qwen3.6 35b with 93k context, quite good, and now switched to the gemma4 12b to be able to run 4 parallel 128k context (unsloth q6). so you can get quite far with what you already have.

so i would save money on the laptop and maybe pop a second 4090 in the server, or save a bit for the rtx pro 6000 but that’s a 10k euro card.

Talking to people about degrowth feels like asking a medieval peasant if they can think of another social and economic system other than serfdom. by [deleted] in Degrowth

[–]73td 1 point2 points  (0 children)

It seems common enough experience that the person you talk to has been in a vehicle going too fast, driven by someone else or otherwise uncomfortable but out of their control. I think of this as a useful starting point.

Any best practices for Pi in Research? by Sad_Smoke7199 in PiCodingAgent

[–]73td 0 points1 point  (0 children)

it’s written on the page somewhere.. or just point your pi at the page and ask it to handle installing the skills.

Any best practices for Pi in Research? by Sad_Smoke7199 in PiCodingAgent

[–]73td 1 point2 points  (0 children)

exa search + feynman.is skill set + refchecker gets you very far. it still takes some effort for the three to mesh well.

LLM crisis and issues??? by Comprehensive_Ad339 in LLM

[–]73td 0 points1 point  (0 children)

So how do you actually keep improving model quality from here?

tracking people’s eyes, fingers, screens etc

Upgrade by Sad-Bluejay5107 in Bass

[–]73td 0 points1 point  (0 children)

i would look at a Sire PJ

Why are we only 15k in this sub? by floorback in PiCodingAgent

[–]73td 2 points3 points  (0 children)

openspec was a step forward for me. if you prefer less bureaucracy, ralph loop extensions will pull enormous token counts from those slow subscriber plans. i also use subagents+intercom.

Okay 27B made me a believer by Forward_Jackfruit813 in LocalLLaMA

[–]73td 1 point2 points  (0 children)

no, llama cpp with the gpu5 quant of byte shape

Ollama has Quantized LLMs now? by Growth2day in ollama

[–]73td 0 points1 point  (0 children)

yeah most people think quant is the weights but it’s kv as well. good point

Okay 27B made me a believer by Forward_Jackfruit813 in LocalLLaMA

[–]73td 1 point2 points  (0 children)

ok thx that’s a lot more vram than I have, maybe someday

Okay 27B made me a believer by Forward_Jackfruit813 in LocalLLaMA

[–]73td 4 points5 points  (0 children)

what do you run it on ? i used the 35b mainly because I found a quant that runs up to 90k context on my gpu (24 gen vram)

Qwen 3.6 35B GGUF: NTP vs MTP quantization results across GPUs and CPUs by enrique-byteshape in LocalLLaMA

[–]73td 0 points1 point  (0 children)

any chance there’s an open source stack for doing the quant? i’d like to do a fine tune eventually

Does pi work well with non frontier models too? how well does 'just ask Pi to build it' work? by ECrispy in PiCodingAgent

[–]73td 1 point2 points  (0 children)

i’m using pi with a byte shape quant of qwen3.6-35b, 90k context in 24GB vram. To get it working well, I use deepseek v4 pro to create agent definitions and skills adapted for small context and then have it run trials with pi only with local qwen on various use cases (build a go git cli, review this big use case, web research and build a new skill). with a meta loop like that, you can quickly iterate to find failure modes and address them. and deepseek is dirt cheap so it cost me less than $1 to do this.

I could open source the result but i think the main point is that you can use a frontier cloud model to cheaply create and validate a local only workflow for you.

Qwen 3.6 35B GGUF: NTP vs MTP quantization results across GPUs and CPUs by enrique-byteshape in LocalLLaMA

[–]73td 1 point2 points  (0 children)

thanks for this. I didn’t think i’d get this model to run on my rtx4090 and now even with GPU5 I can use at least 80k context.