autotrust/GLM5.3-Flash-E256-DGX-Spark is a compact build of zai-org/GLM-5.3-Flash for Blackwell systems, from DGX Spark clusters to a single B200. It's an unofficial derivative. It keeps 256 of the 288 routed experts in each layer by Neural Architecture Search (NAS), uses NVFP4 for the experts and still activates 18 B parameters per token. It's the largest architecture in the family, with the best Chinese, code and vision scores. The KV cache uses FP8. The repo includes a ready-to-use NVFP4 MTP draft in mtp-nvfp4/ (6.95 GB) for speculative decoding. On a single B200 it gives 1.84× single-stream decode speed (1.94× with 3 draft tokens), and output quality is unchanged. The model keeps the…
Open weights
mit
144.5B parameters
1,048,576 tokens
vllm
Model · Image and text to text
Kitani
libraryname: transformers pipelinetag: image-text-to-text basemodel: - Qwen/Qwen3.8-27B - clover - mixture-of-experts - multimodal - reasoning - open-weights - custom-code Clover 1 150B Preview is the first public checkpoint of Clover 1, an experimental MoE model we're developing at Kitani. It's based on Qwen3.8-27B, but it isn't just a finetune with a different name. We kept much of Qwen3.8's underlying architecture, including the tokenizer, multimodal components, hybrid language backbone, embeddings, and LM head, while replacing the language model's dense FFNs with our own sparse mixture-of-experts setup. Clover currently has 8 full-sized experts per language layer with top-1 routing. The…
Open weights
apache-2.0
147.6B parameters
262,144 tokens
transformers
autotrust/GLM5.3-Flash-E224-DGX-Spark is a compact build of zai-org/GLM-5.3-Flash for desktop Blackwell systems like NVIDIA DGX Spark. It's an unofficial derivative. It keeps 224 of the 288 routed experts in each layer by Neural Architecture Search (NAS), uses NVFP4 for the experts and still activates 18 B parameters per token. The weights take 141 GiB, which is small enough for two DGX Sparks connected by ConnectX-7 (256 GB of unified memory in total) with room left for long-context KV cache. A single 180 GB Blackwell GPU (B200/GB200) can also run it. The original GLM-5.3-Flash MTP layer ships unmodified in mtp/ (BF16) as an optional speculative-decoding draft. It gives about 1.85×…
Open weights
mit
127.6B parameters
1,048,576 tokens
vllm
Model · Image and text to text
Qwen
Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. For more details, please refer to our blog post Qwen3.5. WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL. Empty cells (--) indicate scores not yet available or not applicable. Empty cells (--) indicate scores not…
Open weights
apache-2.0
125.1B parameters
262,144 tokens
transformers
Public research conversion of badtheorylabs/Tinfield-1, derived from Qwen/Qwen3.8-Flash-Next. The source checkpoint was pinned to f7c714a97b71342db9e2fc04009d2704c656c80c. This Hugging Face repository contains the complete original export, including all 10 safetensors shards: - PLE embeddings, vision, MTP and other non-expert weights: retained in BF16. - The original configuration, tokenizer, processors and tensor index accompany the shards. The ple-int8-e579 Kaggle variant is a separate quantized PLE derivative. This Hugging Face checkpoint contains BF16 PLE. Load all shards and the configuration from this repository together. Conversion used NVIDIA Model-Optimizer recipe commit…
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other
117B parameters
262,144 tokens
transformers
Model · Image and text to text
Edp1096
Unofficial NVIDIA FP4-Local derivative with Huihui Q8 deltas applied. Applied delta: DQ(Huihui Q8) - DQ(Unsloth Q8). The conversion used these exact revisions. - Updated 44 attention-output/shared-expert-down matrices in layers 15–36 and 22 scale tensors - Preserved all other tensors, including routed experts, CSF scales, MTP, vision, and tokenizer files - Requantized in the existing FP8 formats; no additional QAD training or activation calibration Requires two DGX Sparks (TP2) and B12X vLLM with CSF support. See runtime-configuration.json for exact tested settings. Cold-start loading caused memory pressure and some host swapping. - All 42 weight files verified; 8/8 smoke checks passed…
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other
175.2B parameters
1,048,576 tokens
Model Optimizer
