Over 2k downloads in a day. 🤯
Our first 2-bit model: Qwen3.8-27B in 7.2 GB, 7.3% closer to BF16 Qwen than Bonsai 2.
We will keep producing the best quantizations of open models. This is just the beginning.
https://t.co/HG8NAv6IU0
35.6% closer to the original than Qwen's own FP8.
That's Ingot-V-27B-FP8, the best of 64 quantizations of Qwen3.8-27B that 30 miners built in 6 days.
FP8 round: done, and we're happy with it.
Now we go to 2-bit.
https://t.co/kacPVRp71F
Another model-network kicking off: Ingot (97)
Their latest release Ingot-I quantized Qwen 27B into FP8 better than Alibaba's own quantization in 1 week.
Next target is 2-bit and then the larger GLM models.
Ingot (SN97) has been live for 4 days.
Our current king is already a better quantization of Qwen3.8-27B than Qwen's own FP8 release: 34% closer to the original model.
Same size, more of the original intact. And this is FP8 only.
Lower bits are next. 👀
https://t.co/GcTVCt0xG1
Coding benchmarks only check the final patch.
If an agent finds the right bug but writes the fix wrong, it gets 0.
Same as an agent that never found the bug at all.
Should benchmarks grade the process too?
@shesaidmewakeup Same cutoff, we merge at ≥0.55 and didn't recalibrate.
That's the nice part: Jev gives us the same merges as our LLM aligner at a fraction of the cost, and it's way more decisive. ~99% of pairs come back under 0.4 or over 0.7, so the threshold barely ever comes into play 🔥
Jev is now inside the Albedo validator.
Milestone alignment, question dedup, reference pruning: every pair gets a yes/no probability. Way more deterministic than an LLM, 4x cheaper, a third faster.
Judge stays an LLM. Jev does the classifying around it.
Our judge doesn't just say yes or no. It tells us how sure it is.
Each question gets a score on a 20-level scale, calculated from the judge’s top-20 logprobs rather than a sampled verdict.
That change alone cut score noise by ~20%.
@subnet_mania@engyai Different track. We just made producing kings half the cost, with a stronger judge doing it.
But the benchmarks are getting faster too, trust me on that one ;)
Validator update: GLM-5.2 → GLM-5.3-flash across Albedo.
Half the LLM cost per eval, with a stronger model.
@engyai serves ~70% of our judge calls: ~1B tokens a day, 5× last week’s daily token volume. Thanks, engy ❤️🔥
The teacher is grading now. Join the competiton.