GLM-5.2-SIQ-Fruit

A 5.04B-parameter, 0.46B-active serving proxy for the GLM-5.2 architecture family. Fruit keeps the production-shaped components that matter to the serving stack—MLA attention, the DSA lightning indexer, 256 routed experts with top-8 routing, and one co-trained MTP draft layer—while reducing the hidden size and layer count enough to fit on one consumer GPU.

Runtime requirement: this SIQ/Trellis checkpoint needs a compatible b12x/SparkInfer + vLLM build. Stock vLLM and Transformers do not implement its exl3-trellis expert tensors. For a stock Transformers CPU path, use the BF16 twin.

This is a CI fixture and kernel-development vehicle, not a general assistant.

Releases

Geometry

The parent parameter estimate is derived from its serving configuration; routed experts alone account for about 725B parameters. Fruit is approximately 1:150 by total parameters and 1:91 by active parameters.

Training

Phase 1 ran on 4× NVIDIA H200 spot GPUs on 2026-08-06/07:

1. MAIN: 46,793 steps at 4,096 context, about 4.6B sampled tokens; final global validation loss 2.6577.
2. LONG: 4,500 steps at 16,384 context, about 295M tokens.
3. DISTILL: 1,500 steps at 16,384 context; the DSA indexer was KL-distilled against the dense-attention distribution.
4. QNOISE: 500-step, 49M-token QAT-lite anneal. This repository publishes that annealed checkpoint.

The nine-source pretraining recipe includes FineWeb-Edu, English and Chinese Wikipedia, TinyStories, two GLM-5.2 distillation corpora, REAP calibration text, SPDX license text, and code. Apache-2.0 text was held out as a verbatim-memory probe. The public shard repository omits the gated code shard; see its card for redistribution details.

SIQ artifact

• Non-expert tensors: BF16.
• Routed experts: 96 K4 + 160 K3 in every ordinary MoE layer.
• MTP experts: uniform K3.
• Tensor payload: 3,098,041,856 bytes (2.885 GiB).
• MANIFEST.sha256 authenticates every serving artifact except the card and Git attributes.

The export converts two trainer/serving conventions:

• half-split trainer RoPE to interleaved serving RoPE across 56 projection tensors, with theta 500,000 written to both configuration locations;
• trainer eh_proj(cat[hidden, embed]) to vLLM's eh_proj(cat[embed, hidden]) by swapping the MTP projection's input halves.

Measured validation

Hardware unless noted: RTX 5090; custom gilded-gnosis r25/r28 images.

A deterministic trainer-to-served comparison requested all 154,880 log probabilities at six fixed prediction positions. It measured mean forward $D_{KL}(P_{trainer}\Vert P_{served})$ 0.00132051, maximum 0.00655370, top-1 agreement 6/6, and mean top-10 overlap 98.3%. This is a structural smoke test, not a document-disjoint quality evaluation.

The pre-anneal final checkpoint passed the same serving batteries and measured 97.7% MTP acceptance. It remains available in the checkpoint archive for QNOISE A/B work.

Serving

On a compatible runtime image:

vllm serve malaiwah/GLM-5.2-SIQ-Fruit \
  --kv-cache-dtype fp8_ds_mla

# MTP speculative decoding
vllm serve malaiwah/GLM-5.2-SIQ-Fruit \
  --kv-cache-dtype fp8_ds_mla \
  --speculative-config '{"method":"mtp","num_speculative_tokens":1}'

The r28 validation path also supports nvfp4_ds_mla. A8 activation paths are a separate speed/quality tradeoff and are not used for the codec-quality claims above.

Limitations and intended use

• Small-model answers and short-context smoke results do not establish full-model quality or long-context accuracy.
• The DSA indexer is trained, but the published evidence does not claim document-disjoint task quality.
• The artifact targets serving-stack regression, kernel qualification, and quantization research. Do not deploy it as an assistant.

Reproducibility

The trainer, exporter, parity/KLD probes, smoke suite, and review ledger live at github.com/malaiwah/proxy-fruit (Apache-2.0). The authenticated source checkpoint is final/fruit_v1_annealed.pt, SHA-256 98ac7cb4f7799194424782b505d622069fecf4dbca5f5acb2658f2a66c3631f6. The cross-site trainer suite passed 20/20 cases on 4× RTX 6000 Pro and 17/17 cases on RTX 5090.