GGUF quantizations of Aleph-Alpha/Kolibri-1 — trained from scratch, with tool calling, an explicit reasoning mode and a 262 K native context. Apache-2.0. Because only \~3.5 B parameters are active per token, it decodes like a small model while carrying the knowledge capacity of a large one: \~51 tokens/s on a DGX Spark (GB10) at 4.6 bpw. arch kolibri1 78.1 B params, vocab 128,000 layers 50 FFN MoE 384 experts, top-6, +1 shared (ungated); expert ffn 512 routing select top-6 on logits + per-expert bias, weight by sigmoid(logits), no renorm attention 48 heads / 4 KV, headdim 128, per-head q/k RMSNorm 4 sliding-window layers (window 513, RoPE): 1 full-attention layer (no RoPE) context 262,144…
Independent publisher
Bryan Harris
Myric
I'm interested in running models on hardware I have rather than hardware I can rent (Or maybe in addition to hardware I can rent.)
Models in Library1
Datasets in Library0
Models on Hugging Face30
Followers32
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Published tasks: Text generation.
Reported licenses: apache-2.0. Open a record to review its specific license and conditions.
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