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 Settings

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
```

MMLU PRO Benchmark

by sevapru - opened Feb 11

Discussion

sevapru

Feb 11

•

edited Feb 11

Hi, ran your model for a MMLU PRO benchmark this night (On Jetson Thor in 120W mode)

  MMLU-Pro Benchmark
  Model: GadflyII/Qwen3-Coder-Next-NVFP4
  Endpoint: http://localhost:8868/v1
  Subjects: 14
  Total questions: 12032
  Workers: 16
  Max tokens: 4096
  Subject                    Correct    Wrong   Accuracy
..
  biology                        646       71     90.1%
  business                       654      135     82.9%
  chemistry                      958      174     84.6%
  computer science               345       65     84.1%
  economics                      709      135     84.0%
  engineering                    629      340     64.9%
  health                         621      197     75.9%
  history                        261      120     68.5%
  law                            639      462     58.0%
  math                          1237      114     91.6%
  other                          701      223     75.9%
  philosophy                     368      131     73.7%
  physics                       1125      174     86.6%
  psychology                     644      154     80.7%
...
  TOTAL                         9537     2495    79.26%

Write me if you want to add the information to the model card

GadflyII

Owner Feb 11

Thanks! I will add it to the card!

Did you also test the BF16 as a baseline?

sevapru

Feb 11

No, I have only (lol) 128GB of VRAM, wouldn't fit without swap memory -> will be too painful to run whole bench without concurency and enough caching space

willfalco

Feb 27

•

edited Feb 27

deleted

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