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---
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
```