Niche Model of the Day: Openbuddy 25.2q, QwQ 32B with Quantization Aware Training

Seems there's not a lot of talk about relatively unknown finetunes these days, so I'll start posting more!

Openbuddy's been on my radar, but this one is very interesting: QwQ 32B, post-trained on openbuddy's dataset, apparently with QAT applied (though it's kinda unclear) and context-extended. Observations:

• Quantized with exllamav2, it seems to show lower distortion levels than nomal QwQ. Its works conspicuously well at 4.0bpw and 3.5bpw.
• Seems good at long context. Have not tested 200K, but it's quite excellent in the 64K range.
• Works fine in English.
• The chat template is funky. It seems to mix up the

  <think>

  and <|think|> tags in particular (why don't they just use ChatML?), and needs some wrangling with your own template.
• Seems smart, can't say if it's better or worse than QwQ yet, other than it doesn't seem to "suffer" below 3.75bpw like QwQ does.

Also, I reposted this from /r/locallama, as I feel the community generally should going forward. With its spirit, it seems like we should be on Lemmy instead?