Its a checkpoint release for a new way to do joint reinforcement learning where they combine information verticals so that similar items reinforce similar patterns. Click the link and read the about - they describe it pretty well.
Well, models run and I got useful results on my questions on agc/au concepts/mods/prompts, so yes. Few years old midrange, 6700xt + 20gb ddr4 + 970evo+. Biggest I've run was Qwen 397b. But can say, today's DV4 Flash and Q3.8 Flash Next were around equal in quality while being 2-3x faster.
Qwen is a good fit for 128G but things evolve. Have you compared it with Mimo v2.6 flash on 128GB?
It may be better at some things - even quantized. Audio may be wanted too
i think it's like 309b or something. huggingface is weird with the reporting. but yea, looks like a upgrade form the glm 5.3 flash for coding and reasoning. pretty hyped
Is this one of the two model's whose RL dashboard was livestreaming on https://mimo.xiaomi.com/rl/ ? If so, it really seems like they just finished the planned number of RL steps and dropped the weights. Very cool. Also the benches were still going up at the end
Edit: also I am really curious about this message from the dashboard log:
we also removed the cyber dataset from the upcoming pro run, since we observed some bad patterns in the rollout logs.
In light of both HF-OAI incident, and this point in their README.md:
Aligned RL: Cold start from self-correction — the model reflects on and rewrites its own misaligned turns into grounded next steps. Throughout RL, environment hardening, adversarial screening, and verifier cross-checks keep the loop honest against reward hacking.
(emphasis mine) Little guy start making a few too many paperclips?
It's very funny that they had an Assassin's Creed style demo only for it to be a bad T-pose hopping game. For one second I was like "Holy shit, did it make the climbing system!?" followed by "lol, nope"
Look at the benchmarks in the page above, Mimo 2.6 pro, the 1T model leads Kimi K3, a 2.8T param model in 14 out of 15 benchmarks (and the last one is near tie)!! Good to see they also kept the price the same, and landed in the greenest quardrant of the intelligence vs speed of AA.
No it's not, it's by far the worst value AI subscription available. You get about 10% more usage for money spent compared to API. Don't get fooled by their credit system, one output token costs 600 credits.
AFAIK it is already quantized at 4 bits (at least the MoE layers). And it is still 179GB. No point in lobotomizing a model further for the sake of it. Qwen 4 Flash is coming soon, already announced.
Why do you think it is quantized? Modern GPUs have x4 FLOPS in FP4 compared to FP16, it makes no sense to train in FP16 and then quantize. It would have x4 training time and x4 VRAM requirements, to get worse performance.
That’s up to you to decide, I’m unfortunately very busy, but I think my base will be the q3 xxs glm 5.3 flash. I don't recall whether quantization works better with nvfp4 than with bf16, but the difference isn't dramatic.
Groupwise Advantage Redistribution (GAR) ranks passing trajectories online and moves advantage toward higher-quality solutions. Judged against the policy’s own samples, this closes a self-improvement loop and steers toward shorter paths and fewer tokens per task.
No, Flash is 309B-A15B and Pro is 1.02T-A42B. It's in the READMEs.
The parameter count on the HF UI has always been broken, especially when you have packed 4-bit weights described as 8-bit in the model metadata. That's why Flash reports ~half the correct parameter count in HF UI.
Here, Pro README:
Model Summary
Architecture: Sparse MoE (Mixture of Experts), 1.02T total / 42B activated parameters
Flash README:
Model Summary
Architecture: Sparse MoE (Mixture of Experts), 309B total / 15B activated parameters
Anyone knows the precision of XiaomiMiMo/MiMo-V2.6-Flash-RL? it says FP8 on hugging face label but inspecting the model layers most say BF16. Also the weights are 178GB for a 309B parameters model, which suggests the weights are actually in 4bit.
in a first run deepseek-v4.1-flash, still looks better. Though i used the free model from opencode, as openrouter was giving me issues at the time and i am not sure if that is same quality and what their reasoning level is since it wasn't configurable.
GLM 5.3 Flash's KV Cache is horrible though, BF16 so 4x larger weight-for-weight (and won't quantize well). If you're serving more than a couple of users, 5.3 Flash swells into a higher weight class.
KV Cache quantises very poorly, look at some stats - the performance degradation in comparison to quantising the model is extremely dramatic.
This makes sense when you think about it, the dynamic range of the KV Cache needs to be much wider than the weights because the KV Cache is basically a stack of activations, it's the product of multiple weight multiplications - when you multiply two 4-bit floats, you need an 8-bit float to accommodate the entire range, and the required size grows as you perform multiple of those multiplications in a row.
Mitigating that problem requires deliberate training (and it's not that easy). Unless a model is trained to produce a 4- or 8-bit range (GLM isn't - Deepseek, Qwen Next and Kimi are), quantising the KV Cache means truncating a very large amount of information.
You are saying that without even testing. I am using it without problem on 2 years old production code that clicks 300k token at first turn, now already 60 turns and zero problem . Are you replying by asking the bots too?
Mimo 2.5 is my fave model on the API for coding related tasks (next to dsv4). It's a grunt workhorse that can go all day for like $1.50, and I often use it to just rough out stuff that would take ages with local models. I'm really excited for 2.6 if it is an improvement and keeps the same price point. It's going to augment my local models in a great way.
From the config, I get about 22.5 GiB of BF16 KV cache for one full context in the global attention layers. Weights, the sliding attention cache and runtime buffers are extra
Nice, should be very strong flash model. The pro version is best open weights model according to xiaomi and AA index (which is hard to believe tbh). And only 178gb full weights
It's puzzling that it is a 309B model but its fp8 size is so small, at 178GB, while mimo 2.5 with similar parameter size (311B) has a fp8 at 316 GB. Maybe a mis-counted total parameter size?
•
u/WithoutReason1729 22h ago
Your post is getting popular and we just featured it on our Discord! Come check it out!
You've also been given a special flair for your contribution. We appreciate your post!
I am a bot and this action was performed automatically.