Pinned
DiffusionGemma brings high intelligence and lightning fast ⚡️ inference to local developers (>1100 tok/s on a single H100)!
I'm excited to see what people will do with this model - and what improvements people can build on top (better samplers maybe??).
So unbelievably proud of
GIF
00:08
DiffusionGemma is an open, experimental model that brings our text diffusion research to Gemma 4. It’s a racehorse 🏇achieving up to 4x faster inference by generating entire blocks of text simultaneously vs predicting token-by-token (word-by-word) output!
![for (x1, _) in test_set:
Cx1 = len(gzip.compress(x1.encode()))
distance_from_x1 = []
for (x2, _) in training_set:
Cx2 = len(gzip.compress(x2. encode())
x1x2 = " ".join([x1, x2])
Cx1x2 = len(gzip.compress(x1x2. encode())
ncd = (Cx1x2 - min(Cx1,Cx2)) / max(Cx1, Cx2)
distance_from_x1.append(ncd)
sorted_idx = np.argsort(np.array(distance_from_x1))
top_k_class = training_set[sorted_idx[:k], 1]
predict_class = max(set(top_k_class), key=top_k_class.count)](/Code-https-pbs.twimg.com/media/F05HxqsWwAAHRCI.jpg)
