Instructions to use GadflyII/Qwen3-Coder-Next-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GadflyII/Qwen3-Coder-Next-NVFP4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="GadflyII/Qwen3-Coder-Next-NVFP4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GadflyII/Qwen3-Coder-Next-NVFP4")
model = AutoModelForCausalLM.from_pretrained("GadflyII/Qwen3-Coder-Next-NVFP4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GadflyII/Qwen3-Coder-Next-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GadflyII/Qwen3-Coder-Next-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GadflyII/Qwen3-Coder-Next-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GadflyII/Qwen3-Coder-Next-NVFP4

SGLang

How to use GadflyII/Qwen3-Coder-Next-NVFP4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "GadflyII/Qwen3-Coder-Next-NVFP4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GadflyII/Qwen3-Coder-Next-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "GadflyII/Qwen3-Coder-Next-NVFP4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GadflyII/Qwen3-Coder-Next-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GadflyII/Qwen3-Coder-Next-NVFP4 with Docker Model Runner:
```
docker model run hf.co/GadflyII/Qwen3-Coder-Next-NVFP4
```

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 (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 0.9.0.1.

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+

#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 for the base model
RedHatAI for the quantization approach reference
vLLM Project for llmcompressor

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