https://huggingface.co/unsloth/MiniMax-M2.7-GGUF

I've been using M2.7 through the Alibaba coding plan for a bit now, and am quite impressed with it's coding ability, and even more impressed when I see how small it is. Fascinating really, makes me wonder how big the frontier models are.

What the article describes is that the model was able to tweak to its own deployment harness (memory, skills, experimental loop etc) to improve performance on benchmarks. While impressive, it's not doing any modifications to its own weights by e.g. modifying the training code.

> Non-commercial use permitted based on MIT-style terms; commercial use requires prior written authorization.

And calling the non-commercial usage "MIT-style terms" is a stretch - they come with a bunch of extra restrictions about prohibited uses.

It's open weights, not open source.

I am pretty sure MiniMax M2.7 would be much better.

I had a really bad time with it. I use (real) Claude Code for work so I know what a good model feels like. MiniMax's token plan is nice but the quality is really far from Claude models.

I needed to constantly "remind" it to get things done. Even for a four sentence prompt in a session that is well below the context window, MiniMax would ignore half of it. This happens all the time. (This is Claude Code + MiniMax API, set up using official instructions)

Basically, if I say get A, B and C done, it will only do A and B. I say, you still need to do C, so it does C but reverts the code for A.

Things that Claude can usually one shot takes 5 iterations with MiniMax.

I ended up switching to Claude to get one of my personal projects done.

tiresome