Grug-67B-A2B-Merge-NUPA-BFCL-Shared100-2026.10.09

Model · Text generation

Grug-67B-A2B-Merge-NUPA-BFCL-Shared050-Attn025-2026.10.09

Open Athena

Research artifact. This model has not been properly tested or evaluated and is not necessarily secure. Use at your own risk in production settings. This GrugMoE checkpoint combines Step92, Step12, and Step20 weights through two static merges. It has 26 layers, 256 routed experts per layer, and four selected routed experts per token. The exported weights contain 502 tensors totaling 134,157,778,944 bytes. First, start from Step92 and replace 32 routed experts per layer with Step20 experts selected using BFCL-Parity activation traces. Preserve Step92's remaining weights, router, tokenizer, chat template, and model metadata. Then blend shared-expert projections with 50% Step12 and attention…

Model · Text generation

Grug-67B-A2B-Merge-NUPA-BFCL-Shared075-2026.10.09

Open Athena

Research artifact. This model has not been properly tested or evaluated and is not necessarily secure. Use at your own risk in production settings. This GrugMoE checkpoint combines Step92, Step12, and Step20 weights through two static merges. It has 26 layers, 256 routed experts per layer, and four selected routed experts per token. The exported weights contain 502 tensors totaling 134,157,778,944 bytes. First, start from Step92 and replace 32 routed experts per layer with Step20 experts selected using BFCL-Parity activation traces. Preserve Step92's remaining weights, router, tokenizer, chat template, and model metadata. Then blend each shared-expert projection with 75% Step12 and 25% of…

Model · Text generation

NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4

NVIDIA

For more details on how to deploy and use the model - see the Quick Start Guide below! The post-training data has a cutoff date of February 2026. The pre-training data has a cutoff date of June 2025. NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents. Nemotron-3-Super-120B-A12B-NVFP4 is a large language model (LLM) trained by NVIDIA, designed to deliver strong agentic, reasoning, and conversational capabilities. It is optimized for collaborative agents and high-volume workloads such as IT ticket automation. Like other models in the family, it responds to user queries and…

Model · Text generation

Qwen3.5-122B-A10B-NVFP4

NVIDIA

The NVIDIA Qwen3.5-122B-A10B-NVFP4 model is the quantized version of Alibaba's Qwen3.5-122B-A10B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen3.5-122B-A10B NVFP4 model is quantized with Model Optimizer. This model is ready for commercial/non-commercial use. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (Qwen3.5-122B-A10B) Model Card from Alibaba. Global Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems…

Model · Text generation

Qwen3.5-397B-A17B-VQ-3.1bpw

Noah Zelezny

125.3 GiB text weights — the quality build. Also ships the bf16 vision tower (+0.85 GiB) and an optional MTP draft head (+5.4 GiB); full download 131.6 GiB. A vector-quantized build of Qwen3.5-397B-A17B for machines with memory to spend: the strongest quantization we know how to make of this model at this size, on stock mlx-lm, no patches. 12.43% of the 64-element weight groups in the Qwen3.5-397B teacher sit in output rows whose weights are about 1e-29. The vq-skipzero format drops those rows' codes and scales on disk and in memory; they output exact zeros, and the live rows are byte-identical to the previous revision (vqlab sz-check, every module). Text weights go from 141.71 to 125.31…

Model · Text generation

less-is-moe-qwen3.5-122b-a10b-gpqa-main-64-intdim-g-50

XINKAI ZOU

This checkpoint was structurally pruned with the released Less-is-MoE mean-absolute-gradient method. It removes exactly 50% of routed-expert FFN neurons using 64 calibration samples from the gpqamain configuration of Idavidrein/gpqa revision 633f5ee89ab8ad4522a9f850766b73f62147ffdd. The released loader settings are preserved: train, Question plus shuffled choices, Explanation, selectionseed=1234, BF16, and no optimizer step. The samples are full length: no tokenizer maxlength, truncation, or padding. The longest input for this tokenizer is 1,632 tokens. The source checkpoint was loaded and pruned in BF16. The source-row selection hash is…