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gamansai/
asteria-kimi-k27-brain-sparse

Name: asteria-kimi-k27-brain-sparse
Availability: InStock

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Asteria — Kimi K2.7 Brain (Sparse Slice)

Real brain weights from Kimi K2.7-Code (June 2026, latest Kimi model).

What's in this repo

Source: moonshotai/Kimi-K2.7-Code
Layers: 60 MoE layers (layers 1-60)
Experts per layer: 8 (out of 384)
Total experts: 480
Per expert: 6 .bin files (weight_scale + weight_shape for gate/up/down)
Total size: ~1.3 GB
Format: BF16 (no squeezing, full precision scales)

What's NOT here (the other 99.79% of brain)

weight_packed (the 4-bit weights, 7MB per expert) — fetch on-demand during inference
Other 376 experts per layer — not loaded (sparse loading)
Attention weights (MLA Q/K/V/O) — not loaded
Token embedding (163,840 × 7,168) — not loaded
Output projection (lm_head) — not loaded
Layer 0 (dense, non-MoE) — not loaded

Verification

All weights are REAL trained Kimi K2.7 data:

mean: ~0.006 (matches trained model)
std: ~0.001 (matches trained model)
NOT random init (random would have mean≈0, std≈1)

Usage

python

from huggingface_hub import snapshot_download
import torch, numpy as np

path = snapshot_download(
    repo_id="gamansai/asteria-kimi-k27-brain-sparse",
    allow_patterns="L01/E000/*"
)

raw = open(f"{path}/L01/E000/gate_proj_weight_scale.bin", "rb").read()
u16 = np.frombuffer(raw, dtype=np.uint16)
tensor = torch.from_numpy(u16.copy()).view(torch.bfloat16)
print(f"Shape: {tensor.shape}")
print(f"Mean: {tensor.float().mean():.6f}")  # ~0.007

Transfer details

Transfer date: July 2026
Transfer method: HTTP Range requests from HuggingFace CDN
Speed: 8 parallel workers, ~0.9 experts/sec
Total transfer time: ~25 minutes