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a2genesis/Qwen3.8-27B-NVFP4
Qwen3.8-27B-NVFP4 is a image-text-to-text model from a2genesis. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
NVFP4 quantization of Qwen/Qwen3.8-27B, produced with NVIDIA TensorRT Model Optimizer 0.45 using NVIDIA's own recipe for the qwen35 family (huggingface/qwen35/ptq/w4a16nvfp4-fp8attn-kvfp8cast) — the same mixed-precisi…
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From the Hugging Face model README
NVFP4 quantization of Qwen/Qwen3.8-27B,
produced with NVIDIA TensorRT Model Optimizer 0.45
using NVIDIA's own recipe for the qwen3_5 family
(huggingface/qwen3_5/ptq/w4a16_nvfp4-fp8_attn-kv_fp8_cast) — the same
mixed-precision scheme NVIDIA used for its official Qwen3.6-27B NVFP4 release:
| Component | Precision |
|---|---|
MLP projections (gate/up/down) + lm_head | NVFP4 (W4A16, weight-only, block size 16) |
| Self-attention and linear-attention (GDN) projections | FP8 (E4M3, weights + activations) |
| KV cache | FP8 (constant amax) |
| Vision tower, MTP head, conv/gating layers | BF16 (unquantized) |
Checkpoint size is ~21 GB (vs. ~55 GB BF16).
Post-training quantization with 1024 samples (max. 512 tokens each), 256 each
from four openly available datasets: cnn_dailymail,
Magpie-Align/Magpie-Pro-MT-300K-v0.1, nvidia/OpenCodeReasoning,
nvidia/OpenMathReasoning. Attention implementation during calibration: SDPA.
vllm serve <this-repo> \
--quantization modelopt \
--max-model-len 262144 \
--reasoning-parser qwen3 \
--tool-call-parser qwen3_coder \
--enable-auto-tool-choice
NVFP4 runs natively on Blackwell GPUs; the checkpoint loads on earlier architectures via vLLM's ModelOpt support with reduced benefit.