Hello!

As a handsome local AI enjoyer™ you’ve probably noticed one of the big flaws with LLMs:

It lies. Confidently. ALL THE TIME.

(Technically, it “bullshits” - https://link.springer.com/article/10.1007/s10676-024-09775-5

I’m autistic and extremely allergic to vibes-based tooling, so … I built a thing. Maybe it’s useful to you too.

The thing: llama-conductor

llama-conductor is a router that sits between your frontend (OWUI / SillyTavern / LibreChat / etc) and your backend (llama.cpp + llama-swap, or any OpenAI-compatible endpoint). Local-first (because fuck big AI), but it should talk to anything OpenAI-compatible if you point it there (note: experimental so YMMV).

I tried to make a glass-box that makes the stack behave like a deterministic system, instead of a drunk telling a story about the fish that got away.

TL;DR: “In God we trust. All others must bring data.”

Three examples:

1) KB mechanics that don’t suck (1990s engineering: markdown, JSON, checksums)

You keep “knowledge” as dumb folders on disk. Drop docs (.txt, .md, .pdf) in them. Then:

  • >>attach <kb> — attaches a KB folder
  • >>summ new — generates SUMM_*.md files with SHA-256 provenance baked in
  • `>> moves the original to a sub-folder

Now, when you ask something like:

“yo, what did the Commodore C64 retail for in 1982?”

…it answers from the attached KBs only. If the fact isn’t there, it tells you - explicitly - instead of winging it. Eg:

The provided facts state the Commodore 64 launched at $595 and was reduced to $250, but do not specify a 1982 retail price. The Amiga’s pricing and timeline are also not detailed in the given facts.

Missing information includes the exact 1982 retail price for Commodore’s product line and which specific model(s) were sold then. The answer assumes the C64 is the intended product but cannot confirm this from the facts.

Confidence: medium | Source: Mixed

No vibes. No “well probably…”. Just: here’s what’s in your docs, here’s what’s missing, don’t GIGO yourself into stupid.

And when you’re happy with your summaries, you can:

  • >>move to vault — promote those SUMMs into Qdrant for the heavy mode.

2) Mentats: proof-or-refusal mode (Vault-only)

Mentats is the “deep think” pipeline against your curated sources. It’s enforced isolation:

  • no chat history
  • no filesystem KBs
  • no Vodka
  • Vault-only grounding (Qdrant)

It runs triple-pass (thinker → critic → thinker). It’s slow on purpose. You can audit it. And if the Vault has nothing relevant? It refuses and tells you to go pound sand:

FINAL_ANSWER:
The provided facts do not contain information about the Acorn computer or its 1995 sale price.

Sources: Vault
FACTS_USED: NONE
[ZARDOZ HATH SPOKEN]

Also yes, it writes a mentats_debug.log, because of course it does. Go look at it any time you want.

The flow is basically: Attach KBs → SUMM → Move to Vault → Mentats. No mystery meat. No “trust me bro, embeddings.”

3) Vodka: deterministic memory on a potato budget

Local LLMs have two classic problems: goldfish memory + context bloat that murders your VRAM.

Vodka fixes both without extra model compute. (Yes, I used the power of JSON files to hack the planet instead of buying more VRAM from NVIDIA).

  • !! stores facts verbatim (JSON on disk)
  • ?? recalls them verbatim (TTL + touch limits so memory doesn’t become landfill)
  • CTC (Cut The Crap) hard-caps context (last N messages + char cap) so you don’t get VRAM spikes after 400 messages

So instead of:

“Remember my server is 203.0.113.42” → “Got it!” → [100 msgs later] → “127.0.0.1 🥰”

you get:

!! my server is 203.0.113.42 ?? server ip203.0.113.42 (with TTL/touch metadata)

And because context stays bounded: stable KV cache, stable speed, your potato PC stops crying.


There’s more (a lot more) in the README, but I’ve already over-autism’ed this post.

TL;DR:

If you want your local LLM to shut up when it doesn’t know and show receipts when it does, come poke it:

PS: Sorry about the AI slop image. I can’t draw for shit.

PPS: A human with ASD wrote this using Notepad++. If it the formatting is weird, now you know why.

  • rollin@piefed.social
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    3 days ago

    At first blush, this looks great to me. Are there limitations with what models it will work with? In particular, can you use this on a lightweight model that will run in 16 Gb RAM to prevent it hallucinating? I’ve experimented a little with running ollama as an NPC AI for Skyrim - I’d love to be able to ask random passers-by if they know where the nearest blacksmith is for instance. It was just far too unreliable, and worse it was always confidently unreliable.

    This sounds like it could really help these kinds of uses. Sadly I’m away from home for a while so I don’t know when I’ll get a chance to get back on my home rig.

    • SuspciousCarrot78@lemmy.worldOP
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      3 days ago

      My brother in virtual silicon: I run this shit on a $200 p.o.s with 4gb of VRAM.

      If you can run an LLM at all, this will run. BONUS: because of the way “Vodka” operates, you can run with a smaller context window without eating shit of OOM errors. So…that means… if you could only run a 4B model (because the GGUF itself is 3GBs without the over-heads…then you add in the drag from the KV cache accumulation)… maybe you can now run next sized up model…or enjoy no slow down chats with the model size you have.

      • rollin@piefed.social
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        3 days ago

        I never knew LLMs can run on such low-spec machines now! That’s amazing. You said elsewhere you’re using Qwen3-4B (abliterated), and I found a page saying that there are Qwen3 models that will run on “Virtually any modern PC or Mac; integrated graphics are sufficient. Mobile phones”

        Is there still a big advantage to using Nvidia GPUs? Is your card Nvidia?

        My home machine that I’ve installed ollama on (and which I can’t access in the immediate future) has an AMD card, but I’m now toying with putting it on my laptop, which is very midrange and has Intel Arc graphics (which performs a whole lot better than I was expecting in games)

        • SuspciousCarrot78@lemmy.worldOP
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          3 days ago

          Yep, LLMs can and do run on edge devices (weak hardware).

          One of the driving forces for this project was in fact trying to make my $50 raspberry pi more capable of running llm. It sits powered on all the time, so why not?

          No special magic with NVIDIA per se, other than ubiquity.

          Yes, my card is NVIDIA, but you don’t need a card to run this.