q-bonsai-27b — Bonsai-27B as a κ-object (the full 27B mind, streamed like a film)
Created using Bonsai by Prism ML.
This is prism-ml/Bonsai-27B — a 27-billion-parameter hybrid-attention model (Qwen3.5 family: 16 full-attention + 48 gated-delta linear layers) moved end-to-end into binary {−1,+1} weights (1.125 true bits/weight, embeddings and LM head included) — re-laid as a content-addressed κ-object for the Hologram serverless substrate. No re-quantization anywhere: the trained sign bits pass through byte-exact.
What's here
| File | What it is |
|---|---|
q-bonsai-27b.v1.holo |
ONE Range-streamable file: boot-ordered BLAKE3-verified blocks + embedded tokenizer, lm_head packed early so a progressive reader serves the decode-critical path first. |
manifest.json + b/*.gz |
The same 803 blocks as loose content-addressed parts (sha256 transport axis) — the parallel-fetch fast path on CDN origins. |
manifest.blake3.json + sha256-to-blake3.map.json |
The canonical BLAKE3 (κ) axis. |
tokenizer.gguf |
The source GGUF header (tokenizer + arch), for serverless load. |
Every block is verified against its content address before it is decompressed or touches the GPU. Any static file host serving these bytes is an equal origin — the bytes, not the host, carry identity.
Pins
- manifest κ:
did:holo:sha256:00beea03509a79f6567879f019c04bb15dda8721f6320f8295d9ea1dd9ba54af - root κ (tensor blocks):
sha256:a5f7b8ed89546306702547e1e47541572ef58…(full value in manifest)
Architecture (from the GGUF header)
64 blocks, d=5120, ff=17408, vocab 248320; full_attention_interval=4 → 16 full-attention layers
(24 heads × 256, 4 KV heads, per-head q/k RMSNorm, gated output) + 48 linear layers (gated-delta
SSM: conv kernel 4, state 128, 16 groups, dt-rank 48, inner 6144); MRoPE sections [11,11,10,0],
rope base 1e7; 262K trained context.
Provenance
Weights: Prism ML's Bonsai-27B (Apache-2.0) — see LICENSE and NOTICE.txt.
Conversion: compile2bit.mjs q1 pass-through + holo-kappa-pack.mjs (Hologram Q substrate).
Format q1: blob = [signs N·K/8 B][f32 scales N·K/128·4 B] per tensor; the GEMV kernel
dequantizes inside the matmul. Browser engine support for the hybrid architecture is in
active development on the Hologram substrate; the 8B sibling
(HOLOGRAMTECH/q-bonsai-8b) runs live today.
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We're not able to determine the quantization variants.