--- pipeline_tag: image-text-to-text base_model: - Qwen/Qwen3.5-4B license: apache-2.0 library_name: transformers tags: - AxionML - ModelOpt - Qwen3.5 - quantized - NVFP4 - nvfp4 - sglang --- # AxionML Qwen3.5-4B-NVFP4 > Developed by [AxionML](https://huggingface.co/AxionML) for open-source serving and deployment use cases. Part of AxionML's effort to provide ready-to-serve quantized models for the community. This is an NVFP4-quantized version of [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) (4B parameters), quantized using [NVIDIA TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer). Weights and activations of linear layers are quantized to FP4, reducing disk size and GPU memory by ~4x compared to BF16. **About NVFP4 quantization:** NVFP4 on Blackwell couples a compact E2M1 FP4 codebook with blockwise FP8 (E4M3) scaling over 16-element micro-blocks, so that 4-bit stored values remain numerically useful for neural-network computation. The E2M1 codebook provides a small, nonuniform set of representable magnitudes up to ±6 and relies on saturating behavior rather than IEEE NaN/Inf encodings to maximize usable range per bit. Using an FP8 block scale (rather than power-of-two-only E8M0) enables fractional scales and error-minimizing scale selection strategies such as dual-pass evaluation comparing "map max to 6" versus "map max to 4 with clipping." On Blackwell Tensor Cores, native FP4 multipliers exploit E2M1 simplicity to reduce multiplier area while higher-precision FP32 accumulation protects dot-product accuracy. > **Ready for commercial and non-commercial use under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).** Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. ## Qwen3.5 Highlights Qwen3.5 features the following enhancement: - **Unified Vision-Language Foundation**: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks. - **Efficient Hybrid Architecture**: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead. - **Scalable RL Generalization**: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability. - **Global Linguistic Coverage**: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding. - **Next-Generation Training Infrastructure**: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration. ![Benchmark Results](https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen3.5/Figures/qwen3.5_small_size_score.png) For more details, please refer to our blog post [Qwen3.5](https://qwen.ai/blog?id=qwen3.5). ## Model Overview - Type: Causal Language Model with Vision Encoder - Training Stage: Pre-training & Post-training - Language Model - Number of Parameters: 4B - Hidden Dimension: 2560 - Token Embedding: 248320 (Padded) - Number of Layers: 32 - Hidden Layout: 8 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN)) - Gated DeltaNet: - Number of Linear Attention Heads: 32 for V and 16 for QK - Head Dimension: 128 - Gated Attention: - Number of Attention Heads: 16 for Q and 4 for KV - Head Dimension: 256 - Rotary Position Embedding Dimension: 64 - Feed Forward Network: - Intermediate Dimension: 9216 - LM Output: 248320 (Tied to token embedding) - MTP: trained with multi-steps - Context Length: 262,144 natively and extensible up to 1,010,000 tokens. ## Benchmark Results ### Language
Qwen3.5-4BQwen3.5-4B-NVFP4
Knowledge & STEM
MMLU-Pro 79.1 77.9
MMLU-Redux 88.8 87.0
C-Eval 85.1 83.0
SuperGPQA 52.9 52.4
GPQA Diamond 76.2 74.1
Instruction Following
IFEval 89.8 87.5
IFBench 59.2 58.1
MultiChallenge 49.0 47.8
Long Context
AA-LCR 57.0 56.2
LongBench v2 50.0 49.0
Reasoning & Coding
HMMT Feb 25 74.0 72.7
HMMT Nov 25 76.8 75.4
LiveCodeBench v6 55.8 55.1
OJBench 24.1 23.7
General Agent
BFCL-V4 50.3 49.4
TAU2-Bench 79.9 78.7
VITA-Bench 22.0 21.7
DeepPlanning 17.6 17.4
Multilingualism
MMMLU 76.1 74.8
MMLU-ProX 71.5 70.4
NOVA-63 54.3 53.3
INCLUDE 71.0 69.6
Global PIQA 78.9 77.5
PolyMATH 51.1 49.8
WMT24++ 66.6 64.1
MAXIFE 78.0 75.2

* TAU2-Bench: we follow the official setup except for the airline domain, where all models are evaluated by applying the fixes proposed in the Claude Opus 4.5 system card.

* MMLU-ProX: we report the averaged accuracy on 29 languages.
* WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL.
* MAXIFE: we report the accuracy on English + multilingual original prompts (totally 23 settings).
* Empty cells (--) indicate scores not yet available or not applicable.

### Vision Language
Qwen3.5-4BQwen3.5-4B-NVFP4
STEM and Puzzle
MMMU 77.6 76.1
MMMU-Pro 66.3 65.1
MathVision 74.6 73.0
Mathvista(mini) 85.1 82.8
We-Math 75.4 72.6
DynaMath 83.3 80.3
ZEROBench 3.0 2.9
ZEROBench_sub 26.3 26.0
VlmsAreBlind 92.6 90.6
BabyVision 16.0/19.1 16.0/19.1
General VQA
RealWorldQA 79.5 77.1
MMStar 78.3 77.4
MMBenchEN-DEV-v1.1 89.4 87.0
SimpleVQA 43.4 42.2
HallusionBench 65.0 63.5
Text Recognition and Document Understanding
OmniDocBench1.5 86.2 85.1
CharXiv(RQ) 70.8 69.5
MMLongBench-Doc 54.2 52.9
CC-OCR 76.7 74.6
AI2D_TEST 89.6 88.2
OCRBench 85.0 82.0
Spatial Intelligence
ERQA 54.0 52.4
CountBench 96.3 94.9
RefCOCO(avg) 88.1 85.9
EmbSpatialBench 81.3 78.9
RefSpatialBench 54.6 53.1
LingoQA 74.4 72.2
Hypersim 12.5 12.2
Nuscene 9.9 9.6
Video Understanding
VideoMME(w sub.) 83.5 81.1
VideoMME(w/o sub.) 76.9 75.7
VideoMMMU 74.1 73.0
MLVU 82.8 81.7
MVBench 71.2 69.6
LVBench 66.4 64.6
MMVU 64.9 63.7
Visual Agent
ScreenSpot Pro 60.3 59.3
OSWorld-Verified 35.6 34.9
AndroidWorld 58.6 56.5
Tool Calling
TIR-Bench 38.9/29.9 38.9/29.9
V* 84.3/86.4 84.3/86.4
Medical VQA
SLAKE 76.1 75.1
PMC-VQA 55.5 54.4
MedXpertQA-MM 42.9 41.9

* MathVision: our model’s score is evaluated using a fixed prompt, e.g., “Please reason step by step, and put your final answer within \boxed{}.” For other models, we report the higher score between runs with and without the \boxed{} formatting.
* BabyVision: scores reported as "with CI / without CI".
* TIR-Bench and V*: scores reported as "with CI / without CI".
* Empty cells (--) indicate scores not yet available or not applicable.

## Quantization Details This model was quantized by applying NVFP4 to the weights and activations of linear operators within transformer blocks. The KV-cache is not quantized. Vision encoder weights are kept in their original precision. - **Quantization format:** NVFP4 (MLP-only, MSE calibration) - **Calibration dataset:** [Nemotron-Post-Training-Dataset-v2](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2) - **Quantized checkpoint size:** ~3GB - **Tool:** [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer) ## Usage ### Deploy with SGLang ```bash python3 -m sglang.launch_server \ --model-path AxionML/Qwen3.5-4B-NVFP4 \ --quantization modelopt_fp4 \ --tp 1 \ --reasoning-parser qwen3 ``` ### Reproduce with ModelOpt ```bash python3 examples/llm_ptq/hf_ptq.py \ --pyt_ckpt_path Qwen/Qwen3.5-4B \ --qformat nvfp4_mse \ --export_path ./qwen3.5-4b-nvfp4 ``` ## Limitations The base model was trained on data that may contain toxic language and societal biases. The quantized model inherits these limitations. It may generate inaccurate, biased, or offensive content. Please refer to the [original model card](https://huggingface.co/Qwen/Qwen3.5-4B) for full details.