Very cool to see a company pushing what's possible with (relatively) tiny models! A 350M parameter trained on 28T tokens that, from the benchmarks, is competitive with Qwen3.5-0.8B.
Comparing the architecture to Qwen3.5, it seems:
- fewer, wider layers
- mixing full attention and conv's, instead of the full+linear attention of Qwen3.5
Very cool to see a company pushing what's possible with (relatively) tiny models! A 350M parameter trained on 28T tokens that, from the benchmarks, is competitive with Qwen3.5-0.8B.
Comparing the architecture to Qwen3.5, it seems:
- fewer, wider layers
- mixing full attention and conv's, instead of the full+linear attention of Qwen3.5
- the vocab is about 1/4 the size