VibeusISTA’s Deep Algorithms and Systems Lab has released GSQ-RCO Coder... - Vibeus
ISTA’s Deep Algorithms and Systems Lab has released GSQ-RCO Coder for DeepSeek-V4.1-Flash-1017: half of the routed experts are removed, and the remaining transformer weights occupy 72.1 GB instead of 138.2 GB in the lab’s unpruned IQ2_XXS build.
The original sparse mixture-of-experts model has 763.2B parameters, including 551.9B in the transformer, and activates 6 of 384 routed experts per token. Coder keeps 192 experts in every layer. The “1 bit” headline uses the original transformer as its denominator: 72.12 GB divided by 551.9B parameters is 1.05 bits per parameter, rounded to one. Removed experts count as zero bits. No individual retained weight is stored below one bit; the 280.1B retained transformer parameters average 2.06 bits.
The 72.1 GB figure excludes the shared 110.6 GB Engram file and the vision projector. These are unchanged from the unpruned release and only need downloading once. The model card says the Engram lookup tables can stay on disk; including them, the effective rate across the original whole model is 1.92 bits per parameter.
GSQ, Gumbel-Softmax Quantization, is post-training scalar quantization that learns both grid assignments and group scales. It aims to narrow the low-bit quality gap to vector quantization while using standard GGUF types. RCO, Riemannian Constrained Optimization, chooses which experts survive by minimizing divergence from the unpruned model’s outputs, using task-loss gradients and an exact expert budget for every layer.
Why enforce a separate budget in every layer? GGUF stores one expert count for the entire model. Keeping different counts would require padding, surrendering the memory saved by pruning. RCO holds every layer’s constraint exactly at each optimization step while optimizing all layers jointly through the full-model loss. A penalty-based alternative would only approximate those budgets and require a separately tuned coefficient for each layer.
In the lab’s reported tests, Coder scores 88.57 on LiveCodeBench v6 versus 89.71 for the base model, retaining 98.7% of its score. SWE-bench Verified falls from 81.20 to 77.00, retaining 94.8%. The model card specifies reasoning effort 50 for these measurements. These are the lab’s measurements, rather than an independent replication or a ranking against other models run under different settings.
The larger reduction on SWE-bench Verified comes on long agentic trajectories through real repositories. The lab associates this with errors accumulating across turns: sustained multi-step work tolerates the capacity loss less well than isolated coding problems.
The release also includes examples of single HTML files generated by Coder: an animated SVG pelican on a bicycle, a three.js voxel pagoda garden, and a playable first-person voxel sandbox. In the discussion, u/Loginhe clarifies that these are capability sanity checks beyond benchmarks, rather than additional benchmark evidence; pruning can otherwise wipe out capabilities such as spatial understanding or chat behavior.
The expert-pruned Coder GGUF and the unpruned quantizations are available from ISTA-DASLab on Hugging Face. The repositories for GSQ and RCO include their papers and code, and the Coder repository supplies the complete per-tensor RCO allocation. The stated runtime support is llama.cpp b11870 or newer and Strata. However, u/ruuurbag reports that Coder fails to import in Strata 0.2.9 through both its CLI and desktop app, with an error saying the model type is unsupported.
This is an experimental, capability-targeted release. The model card explicitly accepts degradation outside code, agentic tool use, long context and vision; it recommends the unpruned releases for general-purpose use. Feedback is especially welcome for capabilities missing from the calibration mixture, along with requests for other models to quantize or prune.
