Apache-2.0 This repository contains an NVFP4 quantization of VeriLoop E2, an open 27B post-trained model built on Qwen3.8-27B for code, mathematics, and physics. Its core reasoning discipline is VeriLoop-Governed Recurrence (VGR): candidate states are recursively proposed, externally checked, and retained only when the protected evidence state improves without regression. Quantized by Rodrigo Ramos. Produced with NVIDIA Model Optimizer using the canonical NVFP4W4A4WEIGHTLOCALHESSIANCFG recipe: static per-block 4-bit weights (group size 16) + dynamic 4-bit activations, FP8 attention, local-Hessian calibration with MSE and fp8 scale sweep. The linearattn (Gated Delta Net) blocks and the…
Open weights
apache-2.0
18.3B parameters
262,144 tokens
transformers
Model · Text generation
NVIDIA
The pre-training data has a cutoff date of September 2025. The post-training data has a cutoff date of May 2026. 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. NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 is a large language model (LLM) trained by NVIDIA. The model employs a hybrid Mixture-of-Experts architecture, utilizing interleaved Mamba-2 and MoE layers, along with select Attention layers. The Lightning 3.5 model is released alongside a number of speculative decoding methods for faster text generation. The model has 3B active parameters and 30B parameters in total.…
Open weights
other
17.8B parameters
1,048,576 tokens
transformers
Model · Text generation
NVIDIA
The NVIDIA Qwen3.6-35B-A3B-NVFP4 model is the quantized version of Alibaba's Qwen3.6-35B-A3B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen3.6-35B-A3B-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 (Qwen3.6-35B-A3B) Model Card from Alibaba. Global Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots…
Open weights
apache-2.0
18.7B parameters
262,144 tokens
Model Optimizer
Model · Text generation
Cezar
NVFP4 W4A4 quantization of (revision 9216db5781bf21249d130ec9da846c4624c16137, BF16). It was made for a controlled NVFP4-vs-MXFP4 decode benchmark on NVIDIA B200 (fp4bench), not as a general-purpose release: the NVFP4 and MXFP4 checkpoints share the model, the tool, the recipe, the calibration data and the layer coverage, and differ only in the format. Recipe: QuantizationModifier(targets="Linear", scheme="NVFP4", ignore=["lmhead"]). The plain preset recipe, with no weight-rounding optimization (GPTQ, AutoRound and the like). anon8231489123/ShareGPTVicunaunfiltered at revision 192ab2185289094fc556ec8ce5ce1e8e587154ca, at most 1024 tokens each (33208 tokens in all, seed 3). The data sets…
Open weights
apache-2.0
19.1B parameters
40,960 tokens
An experimental NanoSeedLM (SeedLM) compression of IFM/K2-Horizon-MoVA-36B-A4B for Apple Silicon Macs with 32 GB of unified memory. SeedLM keeps each block of 8 weights as a 16-bit seed of a linear feedback shift register (LFSR), 4 coefficients and an exponent. The GPU makes the weights again from the seed at run time. The paper used an FPGA for this. This model uses Metal kernels on the Mac GPU. This is a research proof of concept. It is not a replacement for a calibrated quantization. - Six safetensors shards (22.76 GB in total, at most 4.5 GB each) and model.safetensors.index.json. The other files are config.json, the tokenizer files of the base model and nanoseedlmk2.py, the MLX loader.…
Open weights
apache-2.0
19.2B parameters
524,288 tokens
mlx
Model · Text generation
Ornith
Chirp Chirp! We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement. Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For…
Open weights
mit
19.5B parameters
262,144 tokens
transformers
