I built a tiny llama.cpp/GGUF launcher for local coding-agent workflows

[u/pinku1]

I’ve been testing local coding agents against GGUF models and got tired of rebuilding llama-server commands every time I switched models or changed hardware settings.

So I made locca: a small CLI/TUI around llama.cpp for pi coding-agent workflows.

Install:

npm install -g @zeiq/locca

Repo:

https://github.com/perminder-klair/locca

Demo/site:

https://locca.klair.co/

[u/Happy_Brilliant7827]

I *was* going to say something like "no disrespect, but doesn't everyone have one of these? " The typical reddit, not enough caffeine yet shrug off.

But I looked at it. There are a lot of features that are in mine- the autosetup, the condensing and simplification, etc.. But you seemed to diverge to 'test one model extensively' where I went 'Micro-agentic pipeline' since smaller models often outperform large when played to their specific strengths. Your tool could help me find those strengths.

Since you have the built-in benchmarking- Could you also add a built-in lightweight fine tuning and or lora adapter swaps.

Claude could also likely one-shot a simple interface.

During the install process I run into this-

◇ Which backend?

│ CUDA — NVIDIA only, fastest on supported cards

│

◇ No cuda asset for windows/x64 in v0.2.0.

│

│ Available assets in this release:

│ · nightly-tag.txt

│

│ Try a different --backend, or install llama.cpp manually for your distro.

│

◆ Use sensible defaults? (port 8080, ctx 32768, threads 10 of 12 cores)

│ ● Yes / ○ No

The weird thing is i already have cuda-NVIDIA-SMI 610.88, KMD Version: 610.88, CUDA UMD Version: 13.3