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Banaxi-Tech 
posted an update 2 days ago
Post
2,226
We're announcing KV1. Now.

We're going to take some models, do a bit of continued training on them and QAT them for only a 2 bit KV cache.
And because of what happened with BGA, and this is also kind of some research stuff, here's what we're going to change:
We're going to always, before we release how and make any claims, release our results.
We also would like (idk if we can) to have some external reviewers before releasing them, I'M talking to YOU @AtAndDev and @mithulaartigala
And we're going to properly cite other research.

The models we are going to release is:
KV1 1.2B, based on LFM 2.5 1.2B Instruct
KV1 2.6B, based on LFM 2.5 2.6B Instruct
KV1 4B, based on Qwen3.5 4B.

If we get enough compute, we may also train:
KV1 9B

And if you guys want to a ~27B model, you guys decide which, either on LFM 2.5 24B A2B, Qwen3.8 27B, or Qwen3.6 35B A3B.

Our goal is to get 2 bit, at 4 bit quality.

Here's an image of our current progress, KV1 is at 3 bit, not 4 bit, but it will improve!

First. Kudos to you. This is research and work we absolute need! ❤️

second. by quantizing the KV-cache that aggressively you are going to slow down an already easy-to-run run 1.2B and 2.6B model.

I am certain that many devices can comfortably run a 1.2B model or a ~3B model. if not you could quantize the model and KV cache (to about 8bits) which will allow the model to fit, but besides that, its the speed which will make it obsolete.
How are you going to adress this, @Banaxi-Tech ?

edit: this would be helpful for a 6B+ model though.

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Well 1.2B and 2B will only be the first tests, on like 256K agentic models it will have a big effect.

for the 27B model. would be great if you could work o the LFM2.5-24B-A2B model!

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That was already going to be what im probrably going to do because of speed.

Get early access to KV1 Models!
https://huggingface.co/kv1-early-access

Interesting direction—especially for long-context agents. I’d report the 2-bit KV result as a small matrix: context length × decode tok/s × peak memory, alongside quality at the same prompts and an 8-bit KV baseline. That would make the speed/quality tradeoff much clearer than a single aggregate score.

Qwen3.8 27B would be much appreciated for actual production workloads.
LFM2.5 24B's speed is impressive, but its performance, specifically system prompt following, is simply not comparable to Qwen3.8.

I run Qwen3.8 27B through IQ4_XS and Q8_0 KV-cache as a daily-driver (simply explaining something for my studied, agentic work e.g. finding bugs, ...), and it's slow (and I can't fit more than 150k tokens of context), so I was excited when LFM's model was published (which I ran @Q4_K_M, unquantized cache), but it failed on even the most basic things (again, for me: explaining something I'm studying, simple agentic work).

From my experience, LFM's models don't take system prompts seriously, and mostly simply ignore them at all.

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Hi, we are very limited on compute so we can't really train big not MoE models.