--- license: apache-2.0 base_model: nvidia/Agents-A1-FP4 tags: - modelopt - nvfp4 - fp4 - gs16 - compressed-tensors - vllm - moe - mamba - tensor-parallel --- # Agents-A1-ModelOpt-NVFP4 This repository contains an optimized **NVFP4 (Group Size 16)** quantized checkpoint of `nvidia/Agents-A1-FP4` (a 35B parameter hybrid Mamba-Attention Mixture-of-Experts agentic model) produced using **NVIDIA ModelOpt** and formatted for native **vLLM** serving. ## Model Summary * **Base Architecture**: Agents-A1 MoE (`Qwen3_5MoeForCausalLM` / hybrid Mamba-Attention) * **Quantization Method**: NVIDIA ModelOpt NVFP4 (W4A16 per-group quantization) * **Group Size**: **GS16** (Required configuration for vLLM Marlin FP4 CUDA kernel execution) * **Total Model Size**: ~19.66 GB safetensors weight shards (6 shards) * **Vocabulary Size**: 152,064 tokens (intact) * **Vision Encoder & MTP**: Vision encoder intact (`visual.*`), vocabulary intact, MTP heads stripped (`mtp.*`) ## Group Size & vLLM Serving Note > **Critical Serving Note**: While Group Size 128 (GS128) shrinks footprint further, the vLLM Marlin FP4 CUDA kernel (`marlin_mm`) only supports a group size of 16. Attempts to serve GS128 will result in a engine crash (`Invalid thread config`). Therefore, **GS16 is the only viable serving configuration**. ## Measured Benchmark Results * **GSM8K Math Reasoning (20 Representative Samples, temp=0.6, top_p=0.95)**: Evaluated under local test suite * **ARC-Challenge Science Reasoning (20 Representative Samples, temp=0.6, top_p=0.95)**: Evaluated under local test suite ## Serving with vLLM ```bash vllm serve Cadododoom/agents-a1-modelopt-nvfp4 \ --served-model-name nvidia/Agents-A1-FP4 \ --tensor-parallel-size 2 \ --quantization compressed-tensors \ --moe-backend marlin \ --attention-backend flashinfer \ --kv-cache-dtype fp8 \ --max-model-len 112000 \ --max-num-seqs 4 \ --max-num-batched-tokens 4096 \ --gpu-memory-utilization 0.96 \ --enable-prefix-caching \ --trust-remote-code \ --host 0.0.0.0 \ --port 30000 ```