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---
library_name: transformers
base_model: Qwen/Qwen3-Coder-Next
tags:
- qwen3
- moe
- nvfp4
- quantized
- llmcompressor
- vllm
license: apache-2.0
pipeline_tag: text-generation
---
# Note: If you have a multi-GPU SM120 Blackwell system (RTX 50/Pro), try my vLLM fork to resolve P2P / TP=2 issues (Pending PR into upstream).
https://github.com/Gadflyii/vllm/tree/main
# Qwen3-Coder-Next-NVFP4
NVFP4 quantized version of [Qwen/Qwen3-Coder-Next](https://huggingface.co/Qwen/Qwen3-Coder-Next) (80B-A3B).
## Model Details
| Property | Value |
|----------|-------|
| **Base Model** | Qwen/Qwen3-Coder-Next |
| **Architecture** | Qwen3NextForCausalLM (Hybrid DeltaNet + Attention + MoE) |
| **Parameters** | 80B total, 3B activated per token |
| **Experts** | 512 total, 10 activated + 1 shared |
| **Layers** | 48 |
| **Context Length** | 262,144 tokens (256K) |
| **Quantization** | NVFP4 (FP4 weights + FP4 activations) |
| **Size** | 45GB (down from ~149GB BF16, 70% reduction) |
| **Format** | compressed-tensors |
## Quantization Details
Quantized using [llmcompressor](https://github.com/vllm-project/llm-compressor) 0.9.0.1.
```python
NUM_CALIBRATION_SAMPLES = 20
MAX_SEQUENCE_LENGTH = 2048
DATASET = "HuggingFaceH4/ultrachat_200k" (train_sft)
moe_calibrate_all_experts = True
# Layers kept in BF16
ignore = [
"lm_head",
"re:.*mlp.gate$", # MoE router gates
"re:.*mlp.shared_expert_gate$", # Shared expert gates
"re:.*linear_attn.*", # DeltaNet linear attention
]
```
## Benchmark Results
### MMLU-Pro
| Model | Accuracy | Delta |
|-------|----------|-------|
| BF16 | 52.90% | - |
| **NVFP4** | **51.27%** | **-1.63%** |
### Context Length Testing
Successfully tested up to **128K tokens** with FP8 KV cache (Not enough VRAM to test any higher context).
## Usage with vLLM
Requires vLLM with NVFP4 support (0.16.0+), Transformers 5.0.0+
```bash
#vllm Serving
vllm serve GadflyII/Qwen3-Coder-Next-NVFP4 \
--tensor-parallel-size 2 \
--max-model-len 131072 \
--kv-cache-dtype fp8
```
## License
Apache 2.0 (same as base model)
## Acknowledgments
- [Qwen Team](https://huggingface.co/Qwen) for the base model
- [RedHatAI](https://huggingface.co/RedHatAI) for the quantization approach reference
- [vLLM Project](https://github.com/vllm-project/vllm) for llmcompressor
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