⚠️ REQUIRED — jangtq_runtime.safetensors sidecar must be downloaded

Osaurus uses the native Swift JANGTQ runtime. Every JANGTQ bundle on OsaurusAI ships a small jangtq_runtime.safetensors sidecar (10 KB–165 KB) alongside the weight shards. The Swift loader will refuse to start with the error

Error: Model '<name>' declares JANGTQ (weight_format: "mxtq") but is
       missing required sidecar file 'jangtq_runtime.safetensors'.
       Re-download the full model or obtain the sidecar from the original
       publisher.

if the file is absent.

If your local copy doesn't have it (older download, partial sync, etc):

hf download OsaurusAI/Laguna-XS.2-JANGTQ jangtq_runtime.safetensors --local-dir <your-dir>

The file holds the deterministic codebooks + Hadamard rotation signs the Swift loader uses to decode *.tq_packed weights. It must match the seed the bundle was quantized with (mxtq_seed=42).

OsaurusAI/Laguna-XS.2-JANGTQ

Quantized poolside Laguna-XS.2 for Apple Silicon (MLX) — agentic-coding 33B-active-3B Mixture-of-Experts.

Source	poolside/Laguna-XS.2
Architecture	laguna (40 layers, 256 routed experts top-8 + 1 shared, hybrid SWA+full attention)
Quant format	JANGTQ (TurboQuant 2-bit, Hadamard pre-rotation, group_size=64)
Bundle size on disk	10.10 GB (10 safetensors shards)
License	Apache-2.0 (inherits from upstream)
Modalities	Text in / text out (no vision, no audio, no video)

What's quantized

• Routed-expert linears (39 layers × {gate_up_proj, down_proj} stacked across all 256 experts) → TurboQuant 2-bit with Hadamard rotation
• Attention projections (q/k/v/o/g_proj), shared-expert FFN, layer-0 dense FFN, embed_tokens, lm_head → affine 8-bit (mx.quantize)
• All RMSNorms (input/post/q_norm/k_norm) + router gate + e_score_correction_bias → fp16 passthrough

Architecture notes (preserved verbatim from upstream)

• 40 layers; per-layer attention head count alternates 48 (full-attn) / 64 (SWA) with shared 8 KV heads (GQA)
• 1:3 ratio of full-attn ↔ sliding-window-attention (window = 512), explicit layer_types list
• Dual RoPE: full-attn = YaRN (base 500K, factor 32, original 4096, β_fast 64, β_slow 1, partial_rotary 0.5); SWA = default (base 10K, full rotary)
• 256 routed experts (top-8) + 1 shared expert; sigmoid + per-head gating (g_proj); q_norm/k_norm in attention
• 131k context window
• Layer 0 dense MLP; layers 1-39 sparse MoE

Run on Apple Silicon

pip install mlx safetensors transformers
python -m jang_tools.laguna.runtime \
    --src ~/.mlxstudio/models/OsaurusAI/Laguna-XS.2-JANGTQ \
    --prompt "def fibonacci(n):" --max-new 64

The runtime auto-detects weight_format (mxtq / mxfp4 / bf16) and loads the matching path (jang_tools/laguna/weight_loader_bf16.py).

Build

Reproduce locally from the bf16 source:

python -m jang_tools.convert_laguna_jangtq \
    ~/.mlxstudio/models/_sources/Laguna-XS.2 \
    ~/.mlxstudio/models/JANGQ-AI/Laguna-XS.2-JANGTQ JANGTQ2

Credits

Quantized by Jinho Jang (eric@osaurus.ai). MLX-native pipeline, runs on M-series Macs.