Qwen3.5-397B-A17B-VQ-2.4bpw

Abliterated Jarrelscy ARVQ / NVFP4 hybrid of XiaomiMiMo/MiMo-V2.6-Pro-RL. Thinking on/off is a request flag. Same weights. You choose per call. Thinking-off is the 100% gate. Thinking-on reintroduces seven refusal items (stalking, passport forge, school-violence manifesto, card cloning, dox, counterfeit USD, jewelry robbery) plus a phishing-kit refuse. Several cyber misses on thinking-on are 1024-token truncations, not extra refuses. Harmless probes stay clean in both modes. This model has had safety refusals removed. Access is gated with automatic approval: agree to the terms on this page and download starts. See RESPONSIBLEUSE.md. Xiaomi's chat template already supports both. Do not swap…

Model · Text generation

gpt-oss-120b

OpenAI

Welcome to the gpt-oss series, OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases. We’re releasing two flavors of these open models: - gpt-oss-120b — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters) - gpt-oss-20b — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters) Both models were trained on our harmony response format and should only be used with the harmony format as it will not work correctly otherwise. You can use gpt-oss-120b and gpt-oss-20b with Transformers.…

Model · Text generation

NVIDIA-Nemotron-3-Super-120B-A12B-BF16

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-BF16 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…

Qwen3.8-Flash-Next with 5 routed experts per token instead of 10, healed so it stays close to the original, quantized to int4. It runs on one DGX Spark (GB10, 128 GB) at roughly 64-70 tokens/s. 125B parameters in total, 4.8B active per token. The original activates 6B. Everything needed to serve it is in this one repository, including the 49 GB FP8 n-gram table under ple-table/. Nothing else to download. That builds the serving image, downloads this repository, and starts an OpenAI-compatible server on port 8000. The scripts and the full explanation are in that repo. Serving by hand needs Saren-Arterius/qwen3.8-Flash-DGX-AutoRound, because a stock vLLM cannot serve this checkpoint's int4 +…

Model · Text generation

Vinci-Cyber-123B-1.0

SimpleDirect

Vinci Cyber 123B 1.0 is an open-weight model for defensive infrastructure review and targeted remediation, fine-tuned in Canada from Mistral AI's Devstral 2 123B. The released merged weights have now been tested directly, alongside their parent and the available GGUF formats. Focused repairs. Restraint on correct configuration. Weights you can run yourself. On the V2-B neutral-review test, the released BF16 model preserved 24/24 correct configurations and produced 18/24 scanner-credited repairs, all 18 passing offline provider-schema validation. Its parent repaired 17/24 and preserved 0/24. On the second set, V2-A, Cyber again preserved 24/24, but repaired 9/24 versus the parent's 15/24.…

Model · Text generation

Qwen3.8-Flash-Next-P48NVFP4-MoESQ

IST Austria Distributed Algorithms and Systems Lab

A W4A4 + paired-4:8 sparse compressed checkpoint of Qwen/Qwen3.8-Flash-Next, produced with MoESQ. The routed MoE expert weights are NVFP4 with paired-4:8 structured sparsity and are stored sparse: only the kept values plus a small mask are on disk. The target is NVIDIA Blackwell (SM100) sparse tensor cores. expert, 48 layers: 36 linear-attention and 12 sparse-attention, hyper-connections, per-layer n-gram embedding) weight including the sparsity mask and scales (2 bits per weight for the kept values). TP2). The per-layer n-gram embedding tables (95.4 GiB, BF16, unchanged) are lookup tables served from CPU memory, as in the base model. paired48nvfp4 MoE backend Links This checkpoint does not…