Piscina-XS.2-GGUF — Laguna XS.2 with Active-Path-Precision Quantization

Piscina shrinks Poolside's 33B-A3B MoE coding model, Laguna XS.2, to as low as ~2.1 bits per weight (8.8 GB) — small enough for consumer GPUs and laptops — using Active-Path-Precision, a MoE-aware mixed-precision recipe, and shows with benchmarks that it stays near-lossless. Built for the Poolside Research Hackathon (Foundations track).

A laguna is a lagoon; a piscina is a pool you can fit at home.

TL;DR

• Result (head-to-head at ~equal size): Piscina-IQ2 cuts KL-divergence 28% (0.555 vs 0.772) and lifts top-1 token agreement +5.3 pts (72.6% vs 67.3%) vs generic IQ2_M at the same size. HumanEval pass@1 90.0% vs 80.0%.
• Active-Path-Precision — keep Laguna's always-active path (attention, router, shared expert, embeddings, output, dense layer 0) near-full-precision (Q6_K/Q8_0) and quantize only the 256 dormant routed experts to ~2-bit. Result: near-lossless quality at the size of a generic 2-bit GGUF.
• Portable GGUF (llama.cpp / Ollama, any NVIDIA/CPU box) and reproducible with llama.cpp --tensor-type overrides (no custom kernels).
• Head-to-head vs generic 2-bit at equal size, measured by KL-divergence + top-1 token agreement vs Q8_0.

Quality vs the Q8_0 reference (wiki corpus, n_ctx=512)

Variant	Method	bpw	Size (GB)	Mean KL-div ↓	Top-1 agree ↑	PPL ↓	tok/s ↑
Q8_0 (ref)	reference	~8.5	35.6	0.000	100%	14.38	160
Piscina-IQ2	Active-Path-Precision	~2.9	11.9	0.555	72.6%	17.06	149
IQ2_M	generic uniform	~2.7	11.0	0.772	67.3%	17.19	156
Piscina-IQ1	Active-Path-Precision	~2.1	8.8	1.347	58.0%	26.31	151
IQ1_M	generic uniform	~1.75	7.6	2.454	42.6%	56.89	164

Recommended pick: Piscina-IQ2 — near-lossless on a 16 GB GPU. Piscina-IQ1 for 12 GB. Generic IQ1_* only when memory is the hard constraint.

Usage (llama.cpp)

hf download poolside-laguna-hackathon/Piscina-XS.2-GGUF Piscina-XS.2-IQ2.gguf --local-dir .
# requires a laguna-aware llama.cpp build (see Credits)
./llama-server -m Piscina-XS.2-IQ2.gguf -ngl 99 -c 8192 --port 8080

Method — Active-Path-Precision

Plain sub-4-bit quantization treats every tensor equally and crushes the parts of a Mixture-of-Experts that are most fragile. Active-Path-Precision allocates bits by how often a tensor is on the active compute path:

• High precision (Q6_K / Q8_0): attention (q/k/v/output), the router (ffn_gate_inp, the most quantization-sensitive component), the shared expert (fires every token), the leading dense layer 0, token embeddings and the output head.
• Aggressive (~2-bit, IQ2_M / IQ1_M): the 256 dormant routed experts (ffn_{gate,up,down}_exps), which hold most parameters but each activate rarely.

Routed experts dominate the parameter count, so the model still lands at ~2-3 bpw and fits a 16 GB GPU, while the per-token compute path stays near-full-precision — hence near-lossless quality. Stock llama.cpp --tensor-type overrides + an imatrix; no custom kernels.

Recipe (flagship Piscina-IQ2):

llama-quantize --imatrix author.imatrix \
  --token-embedding-type q6_K --output-tensor-type q6_K \
  --tensor-type attn=q6_K --tensor-type ffn_gate_inp=q8_0 \
  --tensor-type shexp=q6_K --tensor-type blk.0.ffn=q6_K \
  Laguna-XS.2-f16.gguf Piscina-XS.2-IQ2.gguf IQ2_M

Grounded in recent MoE-quantization literature: MoQE (arXiv:2310.02410), Examining MoE quantization (arXiv:2406.08155), QMoE (arXiv:2310.16795), MxMoE (arXiv:2505.05799), EAQuant (arXiv:2506.13329, router fragility → validate with KL-divergence).

Limitations (honest)

• 1-bit variants (IQ1_*) show a sharp quality cliff — published to map where Laguna breaks, not for production.
• KL-div/top-1 measured on a wiki corpus at n_ctx=512; treat as directional.
• KV cache / long-context memory is separate from weight size; budget VRAM accordingly.

Functional code quality — HumanEval pass@1

Directional subset (n=20 problems), greedy decoding, served via the laguna-aware llama.cpp llama-server with the model's chat template.

Variant	HumanEval pass@1 ↑
Q8_0 (reference)	95.0%
Piscina-IQ2 (active-path)	90.0%
IQ2_M (generic)	80.0%

The Laguna XS.2 model card reports SWE-bench Verified/Multilingual/Pro and Terminal-Bench 2.0. Those require full Dockerized agentic harnesses (hours of compute) and are out of scope for this hackathon's time/compute budget. HumanEval pass@1 is included as a lightweight functional proxy for how well code-generation quality survives quantization.

Credits

Base model: poolside/Laguna-XS.2 (Apache 2.0). Source f16 GGUF: linuxid10t/Laguna-XS.2-GGUF. Runtime: laguna-aware llama.cpp fork linuxid10t/llama.cpp-add-laguna (mainline llama.cpp does not yet support the laguna architecture). imatrix recomputed from the f16 on wiki calibration text. Built for the Poolside Research Hackathon.