reddit

<!-- SC_OFF --><div class="md"><p>Hi, I am a PhD student and trying to run a ML reading group focused on interpretability and robustness every weekend. Its always nice to hear different takes and opinions on a paper and this discussion group could serve the purpose. If you are a

<!-- SC_OFF --><div class="md"><p>Anyone check out the new Gemma4 12B that dropped 3 days ago? Integrated vision and audio recognition, no mmpro needed plus tool use. Q4 quant is like 8gb RAM. Crazy fast and great quality for it's size. No, it's not as good as a 27B or 31B. But i

<!-- SC_OFF --><div class="md"><p>It is kind of stupid at first but it's probably the best example of a model that seems smarter as it gets more context. Rather than getting stupider with more context it doesn't seem to hit its stride until about 70-90k tokens. </p> <p>Curious if

<!-- SC_OFF --><div class="md"><p>One thing I've noticed after building software for founders:</p> <p>Most people don't fail because their idea is bad.</p> <p>They fail because they spend 3–6 months building features nobody asked for.</p> <p>A few weeks ago I was helping someone

<!-- SC_OFF --><div class="md"><p>I’m graduating with a CIS Tech degree and was offered a 45-day contract position with a small MSP. The company is only a handful of people from what I can tell. I’ve gone through multiple interviews, reference checks, and received an offer, but I

<!-- SC_OFF --><div class="md"><p>Edited -</p> <p>After digging into this some more and reviewing unsloth post for better understanding, the divergence APPEARS to stem from I did not use the BF16 QAT model as the &quot;reference&quot; model....</p> <p>The QAT vs standard Q4 compa

<!-- SC_OFF --><div class="md"><p>I’m in the process of launching a context memory tool called spiderbrain ai (launches on June 10th) and currently finalizing the video for our launch. I created a ai generated ad which turned out great to explain the benefits but I feel it lacked