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AxionML
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Model Details
Model Provider
AxionML
Model Tree
Qwen/Qwen3.5-0.8B-Base
Input Modalities
Output Modalities
Supported Functionality
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.
For more details, please refer to our blog post Qwen3.5.
If you find our work helpful, feel free to give us a cite.
bibtex
@misc{qwen3.5,title = {{Qwen3.5}: Towards Native Multimodal Agents},author = {{Qwen Team}},month = {February},year = {2026},url = {https://qwen.ai/blog?id=qwen3.5}}
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.
bash
python3 -m sglang.launch_server \--model-path AxionML/Qwen3.5-0.8B-Base-NVFP4 \--quantization modelopt_fp4 \--tp 1 \--reasoning-parser qwen3
bash
python3 examples/llm_ptq/hf_ptq.py \--pyt_ckpt_path Qwen/Qwen3.5-0.8B-Base \--qformat nvfp4_mse \--export_path ./qwen3.5-0.8b-base-nvfp4
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 for full details.