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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.

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[–] PolarKraken@lemmy.dbzer0.com 1 points 3 months ago (1 children)

This sounds really interesting, I'm looking forward to reading the comments here in detail and looking at the project, might even end up incorporating it into my own!

I'm working on something that addresses the same problem in a different way, the problem of constraining or delineating the specifically non-deterministic behavior one wants to involve in a complex workflow. Your approach is interesting and has a lot of conceptual overlap with mine, regarding things like strictly defining compliance criteria and rejecting noncompliant outputs, and chaining discrete steps into a packaged kind of "super step" that integrates non-deterministic substeps into a somewhat more deterministic output, etc.

How involved was it to build it to comply with the OpenAI API format? I haven't looked into that myself but may.

[–] SuspciousCarrot78@lemmy.world 1 points 3 months ago* (last edited 3 months ago)

Cheers!

Re: OpenAI API format: 3.6 - not great, not terrible :)

In practice I only had to implement a thin subset: POST /v1/chat/completions + GET /v1/models (most UIs just need those). The payload is basically {model, messages, temperature, stream...} and you return a choices[] with an assistant message. The annoying bits are the edge cases: streaming/SSE if you want it, matching the error shapes UIs expect, and being consistent about model IDs so clients don’t scream “model not found”. Which is actually a bug I still need to squash some more for OWUI 0.7.2. It likes to have its little conniptions.

But TL;DR: more plumbing than rocket science. The real pain was sitting down with pen and paper and drawing what went where and what wasn't allowed to do what. Because I knew I'd eventually fuck something up (I did, many times), I needed a thing that told me "no, that's not what this is designed to do. Do not pass go. Do not collect $200".

shrug I tried.

[–] floquant@lemmy.dbzer0.com 1 points 3 months ago* (last edited 3 months ago) (1 children)

Holy shit I'm glad to be on the autistic side of the internet.

Thank you for proving that fucking JSON text files are all you need and not "just a couple billion more parameters bro"

Awesome work, all the kudos.

[–] SuspciousCarrot78@lemmy.world 1 points 3 months ago

Thanks. It's not perfect but I hope it's a step in a useful direction

[–] domi@lemmy.secnd.me 0 points 3 months ago (5 children)

I have a Strix Halo machine with 128GB VRAM so I'm definitely going to give this a try with gpt-oss-120b this weekend.

[–] recklessengagement@lemmy.world 1 points 3 months ago (1 children)

Strix halo gang. Out of curiosity, what OS are you using?

[–] domi@lemmy.secnd.me 1 points 3 months ago

Fedora 43 with the Rawhide kernel.

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[–] BaroqueInMind@piefed.social 0 points 3 months ago (2 children)

I have no remarks, just really amused with your writing in your repo.

Going to build a Docker and self host this shit you made and enjoy your hard work.

Thank you for this!

[–] SuspciousCarrot78@lemmy.world 1 points 3 months ago

Thank you <3

Please let me know how it works...and enjoy the >>FR settings. If you've ever wanted to trolled by Bender (or a host of other 1990s / 2000s era memes), you'll love it.

[–] Diurnambule@jlai.lu 0 points 3 months ago (1 children)

Same sentiment. Tonight it run on my systems XD.

[–] SuspciousCarrot78@lemmy.world 1 points 3 months ago* (last edited 3 months ago)

There are literally dozens of us. DOZENS!

I'm on a potato, so I can't attach it to something super sexy, like a 405B or a MoE.

If you do, please report back.

PS: You may see (in the docs) occasional references that slipped passed me to MoA. That doesn't stand for Mixture of Agents. That stood for "Mixture of Assholes". That's always been my mental model for this.

Or, in the language of my people, this was my basic design philosophy:

YOU (question)-> ROUTER+DOCS (Ah shit, here we go again. I hate my life)

|

ROUTER+DOCS -> Asshole 1: Qwen ("I'm right")

|

ROUTER+DOCS -> Asshole 2: Phi ("No, I'm right")

|

ROUTER+DOCS -> Asshole 3: Nanbeige ("Idiots, I'm right!")

|

ROUTER+DOCS (Jesus, WTF. I need booze now) <- (all assholes)

|

--> YOU (answer)

(this could have been funnier in the ASCII actually worked but man...Lemmy borks that)

EDIT: If you want to be boring about it, it's more like this

https://pastebin.com/gNe7bkwa

PS: If you like it, let other people in other places know about it.

[–] WolfLink@sh.itjust.works 0 points 3 months ago (1 children)

I’m probably going to give this a try, but I think you should make it clearer for those who aren’t going to dig through the code that it’s still LLMs all the way down and can still have issues - it’s just there are LLMs double-checking other LLMs work to try to find those issues. There are still no guarantees since it’s still all LLMs.

[–] skisnow@lemmy.ca 0 points 3 months ago (1 children)

I haven’t tried this tool specifically, but I do on occasion ask both Gemini and ChatGPT’s search-connected models to cite sources when claiming stuff and it doesn’t seem to even slightly stop them bullshitting and claiming a source says something that it doesn’t.

[–] SuspciousCarrot78@lemmy.world 0 points 3 months ago (1 children)

Yeah, this is different. Try it. It gives you cryptogenic key to the source (which you must provide yourself: please be aware. GIGO).

[–] skisnow@lemmy.ca 0 points 3 months ago* (last edited 3 months ago) (1 children)

How does having a key solve anything? Its not that the source doesn’t exist, it’s that the source says something different to the LLM’s interpretation of it.

[–] SuspciousCarrot78@lemmy.world 1 points 3 months ago* (last edited 3 months ago)

Yeah.

The SHA isn’t there to make the model smarter. It’s there to make the source immutable and auditable.

Having been burnt by LLMs (far too many times), I now start from a position of "fuck you, prove it".

The hash proves which bytes the answer was grounded in, should I ever want to check it. If the model misreads or misinterprets, you can point to the source and say “the mistake is here, not in my memory of what the source was.”.

If it does that more than twice, straight in the bin. I have zero chill any more.

Secondly, drift detection. If someone edits or swaps a file later, the hash changes. That means yesterday’s answer can’t silently pretend it came from today’s document. I doubt my kids are going to sneak in and change the historical prices of 8 bit computers (well, the big one might...she's dead keen on being a hacker) but I wanted to be sure no one and no-thing was fucking with me.

Finally, you (or someone else) can re-run the same question against the same hashed inputs and see if the system behaves the same way.

So: the hashes don't fix hallucinations (I don't even think that's possible, even with magic). The hashes make it possible to audit the answer and spot why hallucinations might have happened.

PS: You’re right that interpretation errors still exist. That's why Mentats does the triple-pass and why the system clearly flags “missing / unsupported” instead of filling gaps. The SHA is there to make the pipeline inspectable, instead of “trust me, bro.”.

Guess what? I don't trust you. Prove it or GTFO.

[–] itkovian@lemmy.world 0 points 3 months ago (1 children)

Based AF. Can anyone more knowledgeable explain how it works? I am not able to understand.

[–] SuspciousCarrot78@lemmy.world 0 points 3 months ago (1 children)

Hell yes I can explain. What would you like to know.

[–] itkovian@lemmy.world 0 points 3 months ago (1 children)

As I understand it, it corrects the output of LLMs. If so, how does it actually work?

[–] SuspciousCarrot78@lemmy.world 0 points 3 months ago (1 children)

Good question.

It doesn’t “correct” the model after the fact. It controls what the model is allowed to see and use before it ever answers.

There are basically three modes, each stricter than the last. The default is "serious mode" (governed by serious.py). Low temp, punishes chattiness and inventiveness, forces it to state context for whatever it says.

Additionally, Vodka (made up of two sub-modules - "cut the crap" and "fast recall") operate at all times. Cut the crap trims context so the model only sees a bounded, stable window. You can think of it like a rolling, summary of what's been said. That summary is not LLM generated summary either - it's concatenation (dumb text matching), so no made up vibes.

Fast recall OTOH stores and recalls facts verbatim from disk, not from the model’s latent memory.

It writes what you tell it to a text file and then when you ask about it, spits it back out verbatim ((!! / ??)

And that's the baseline

In KB mode, you make the LLM answer based on the above settings + with reference to your docs ONLY (in the first instance).

When you >>attach , the router gets stricter again. Now the model is instructed to answer only from the attached documents.

Those docs can even get summarized via an internal prompt if you run >>summ new, so that extra details are stripped out and you are left with just baseline who-what-where-when-why-how.

The SUMM_*.md file come SHA-256 provenance, so every claim can be traced back to a specific origin file (which gets moved to a subfolder)

TL;DR: If the answer isn’t in the KB, it’s told to say so instead of guessing.

Finally, Mentats mode (Vault / Qdrant). This is the “I am done with your shit" path.

It's all of the three above PLUS a counter-factual sweep.

It runs ONLY on stuff you've promoted into the vault.

What it does is it takes your question and forms in in a particular way so that all of the particulars must be answered in order for there to BE an answer. Any part missing or not in context? No soup for you!

In step 1, it runs that past the thinker model. The answer is then passed onto a "critic" model (different llm). That model has the job of looking at the thinkers output and say "bullshit - what about xyz?".

It sends that back to the thinker...who then answers and provides final output. But if it CANNOT answer the critics questions (based on the stored info?). It will tell you. No soup for you, again!

TL;DR:

The “corrections” happen by routing and constraint. The model never gets the chance to hallucinate in the first place, because it literally isn’t shown anything it’s not allowed to use. Basic premise - trust but verify (and I've given you all the tools I could think of to do that).

Does that explain it better? The repo has a FAQ but if I can explain anything more specifically or clearly, please let me know. I built this for people like you and me.

[–] itkovian@lemmy.world 0 points 3 months ago* (last edited 3 months ago) (1 children)

That is much clearer. Thank you for making this. It actually makes LLMs useful with much lesser downsides.

[–] SuspciousCarrot78@lemmy.world 1 points 3 months ago (1 children)

God, I hope so. Else I just pissed 4 months up the wall and shouted a lot of swears at my monitor for nada :)

Let me know if it works for you

[–] itkovian@lemmy.world 1 points 3 months ago
[–] ThirdConsul@lemmy.zip 0 points 3 months ago (4 children)

I want to believe you, but that would mean you solved hallucination.

Either:

A) you're lying

B) you're wrong

C) KB is very small

[–] SuspciousCarrot78@lemmy.world 1 points 3 months ago (1 children)

D) None of the above.

I didn’t "solve hallucination". I changed the failure mode. The model can still hallucinate internally. The difference is it’s not allowed to surface claims unless they’re grounded in attached sources.

If retrieval returns nothing relevant, the router forces a refusal instead of letting the model free-associate. So the guarantee isn’t “the model is always right.”

The guarantee is “the system won’t pretend it knows when the sources don’t support it.” That's it. That's the whole trick.

KB size doesn’t matter much here. Small or large, the constraint is the same: no source, no claim. GTFO.

That’s a control-layer property, not a model property. If it helps: think of it as moving from “LLM answers questions” to “LLM summarizes evidence I give it, or says ‘insufficient evidence.’”

Again, that’s the whole trick.

You don't need to believe me. In fact, please don't. Test it.

I could be wrong...but if I'm right (and if you attach this to a non-retarded LLM), then maybe, just maybe, this doesn't suck balls as much as you think it might.

Maybe it's even useful to you.

I dunno. Try it?

[–] ThirdConsul@lemmy.zip 0 points 3 months ago (1 children)

So... Rag with extra steps and rag summarization? What about facts that are not rag retrieval?

[–] SuspciousCarrot78@lemmy.world 1 points 3 months ago* (last edited 3 months ago) (5 children)

Parts of this are RAG, sure

RAG parts:

  • Vault / Mentats is classic retrieval + generation.
  • Vector store = Qdrant
  • Embedding and reranker

So yes, that layer is RAG with extra steps.

What’s not RAG -

KB mode (filesystem SUMM path)

This isn’t vector search. It’s deterministic, file-backed grounding. You attach folders as needed. The system summarizes and hashes docs. The model can only answer from those summaries in that mode. There’s no semantic retrieval step. It can style and jazz around the answer a little, but the answer is the answer is the answer.

If the fact isn’t in the attached KB, the router forces a refusal. Put up or shut up.

Vodka (facts memory)

That’s not retrieval at all, in the LLM sense. It's verbatim key-value recall.

  • JSON on disk
  • Exact store (!!)
  • Exact recall (??)

Again, no embeddings, no similarity search, no model interpretation.

"Facts that aren’t RAG"

In my set up, they land in one of two buckets.

  1. Short-term / user facts → Vodka. That for things like numbers, appointments, lists, one-off notes etc. Deterministic recall, no synthesis.

  2. Curated knowledge → KB / Vault. Things you want grounded, auditable, and reusable.

In response to the implicit "why not just RAG then"

Classic RAG failure mode is: retrieval is fuzzy → model fills gaps → user can’t tell which part came from where.

The extra "steps" are there to separate memory from knowledge, separate retrieval from synthesis and make refusal a legal output, not a model choice.

So yeah; some of it is RAG. RAG is good. The point is this system is designed so not everything of value is forced through a semantic search + generate loop. I don't trust LLMs. I am actively hostile to them. This is me telling my LLM to STFU and prove it, or GTFO. I know that's a weird way to operate maybe (advesarial, assume worst, engineer around issue) but that's how ASD brains work.

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[–] FrankLaskey@lemmy.ml 0 points 3 months ago (1 children)

This is very cool. Will dig into it a bit more later but do you have any data on how much it reduces hallucinations or mistakes? I’m sure that’s not easy to come by but figured I would ask. And would this prevent you from still using the built-in web search in OWUI to augment the context if desired?

[–] SuspciousCarrot78@lemmy.world 0 points 3 months ago (1 children)

Comment removed by (auto-mod?) cause I said sexy bot. Weird.

Restating again: On the stuff you use the pipeline/s on? About 85-90% in my tests. Just don't GIGO (Garbage in, Garbage Out) your source docs...and don't use a dumb LLM. That's why I recommend Qwen3-4 2507 Instruct. It does what you tell it to (even the abilterated one I use).

[–] 7toed@midwest.social 0 points 3 months ago (1 children)

abilterated one

Please elaborate, that alone piqued my curiosity. Pardon me if I couldve searched

[–] SuspciousCarrot78@lemmy.world 0 points 3 months ago* (last edited 3 months ago) (1 children)

Yes of course.

Abliterated is a technical LLM term meaning "safety refusals removed".

Basically, abliteration removes the security theatre that gets baked into LLM like chatGPT.

I don't like my tools deciding for me what I can and cannot do with them.

I decide.

Anyway, the model I use has been modified with a newer, less lobotomy inducing version of abliteration (which previously was a risk).

https://huggingface.co/DavidAU/Qwen3-4B-Hivemind-Instruct-NEO-MAX-Imatrix-GGUF/tree/main

According to validation I've seen online (and of course, I tested it myself), it's lost next to zero "IQ" and dropped refusals by about...90%.

BEFORE: Initial refusals: 99/100

AFTER: Refusals: 8/100 [lower is better], KL divergence: 0.02 (less than 1 is great, "0" is perfect.)

In fact, in some domains it's actually a touch smarter, because it doesn't try to give you "perfect" model answers. Maths reasoning for example, where the answer is basically impossible, it will say "the answer is impossible. Here's the nearest workable solution based on context" instead of getting stuck in a self-reinforcing loop, trying to please you, and then crashing.

In theory, that means you could ask it for directions on how to cook Meth and it would tell you.

I'm fairly certain the devs didn't add the instructions for that in there, but if they did, the LLM won't "sorry, I can't tell you, Dave".

Bonus: with my harness over the top, you'd have an even better idea if it was full of shit (it probably would be, because, again, I'm pretty sure they don't train LLM on Breaking Bad).

Extra double bonus: If you fed it exact instructions for cooking meth, using the methods I outlined? It will tell you exactly how to cook Meth, 100% of the time.

Say...you...uh...wanna cook some meth? :P

PS: if you're more of a visual learner, this might be a better explanation

https://www.youtube.com/watch?v=gr5nl3P4nyM

[–] 7toed@midwest.social 1 points 3 months ago (1 children)

Thank you again for your explainations. After being washed up with everything AI, I'm genuinely excited to set this up. I know what I'm doing today! I will surely be back

[–] SuspciousCarrot78@lemmy.world 1 points 3 months ago

Please enjoy. Make sure you use >>FR mode at least once. You probably won't like the seed quotes but maybe just maybe you might and I'll be able to hear the "ha" from here.

[–] als@lemmy.blahaj.zone 0 points 3 months ago

neat but is this privacy related?

[–] rozodru@piefed.social 0 points 3 months ago (1 children)

soooo if it doesn't know something it won't say anything and if it does know something it'll show sources...so essentially you plug this into Claude it's just never going to say anything to you ever again?

neat.

[–] SuspciousCarrot78@lemmy.world 1 points 3 months ago

I see what you did there :)

Claude! Look how they massacred my boy!

[–] termaxima@slrpnk.net 0 points 3 months ago (1 children)

Hallucination is mathematically proven to be unsolvable with LLMs. I don't deny this may have drastically reduced it, or not, I have no idea.

But hallucinations will just always be there as long as we use LLMs.

[–] SuspciousCarrot78@lemmy.world 1 points 3 months ago* (last edited 3 months ago)

Agree-ish

Hallucination is inherent to unconstrained generative models: if you ask them to fill gaps, they will. I don’t know how to “solve” that at the model level.

What you can do is make “I don’t know” an enforced output, via constraints outside the model.

My claim isn’t “LLMs won’t hallucinate.” It’s “the system won’t silently propagate hallucinations.” Grounding + refusal + provenance live outside the LLM, so the failure mode becomes “no supported answer” instead of “confident, slick lies.”

So yeah: generation will always be fuzzy. Workflow-level determinism doesn’t have to be.

I tried yelling, shouting, and even percussive maintenance but the stochastic parrot still insisted “gottle of geer” was the correct response.

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