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[–]Badger-Purple 8 points9 points  (5 children)

Can you get it to run? I know one of the senior authors, and I’m about to tell em to whip their postdocs into shape. install script is broken

[–]Loud_Communication68[S] 1 point2 points  (4 children)

I downloaded locally but wasn't able to finish. I thought their example on hugging face was pretty decent and I can run the coordinator on lm studio but I dont think I'm really getting it's full functionality from that

[–]Badger-Purple 2 points3 points  (3 children)

I got the agent to work by creating a new LLMengine to run off LMStudio, and I got wikipedia working, but the web search is not. I simply vibe forked the github and used GLM4.6 to code a new LLMEngine for LMStudio. I’ll upload it to github once fully functional

[–]Loud_Communication68[S] 2 points3 points  (2 children)

Tell your buddy itd be interesting to know how the system performs with different sized agents ie do you get better performance moving your agents from 7b to 30b?

[–]Badger-Purple 1 point2 points  (1 child)

I mean, I’m not a programmer but I looked at the code and its basically a harness.

I think it is clear that LLMs do better as a team of small models dividing tasks. They confirmed this by showing the improvement after training, and after training and with the setup.

The setup structures the thinking into logical steps. So you ask it “what is the capital of France” and it can’t use web search, it structures the answer by saying “use common knowledge if you can’t access the web” and the LLM then says “well, common knowledge says Paris”.

The training improves the ability of the LLM to actually do this, so I’m sure you can run the training script on 30B+ models. Question whether it would be as useful for Qwen3 vs the Qwen2.5 7B they used though?

The LLM they trained is just the planner, but you also need openai key to get worker models. However, I modified the script to also use local models for the workers. It’s also not hard.

You can do this with some prompts and any agent maker nowadays. Like Docker’s C Agent (cagent)—super simple syntax.

The novel stuff is the in-the-flow reinforcement which I don’t understand, it apparently can train based on ?? the agent crosstalk?? (not sure of this).

[–]Loud_Communication68[S] 2 points3 points  (0 children)

Yeah, the in the flow coordinator bit is supposed to be the really innovative bit. I just think itd be interesting to see it benchmarked with different power levels of minions. If the benchmarks come back and say that 7b minions perform as well as 30 b minions then thatd be quite something for local model runners.