Instructions to use webbrain-one/Nanbeige4.3-33B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use webbrain-one/Nanbeige4.3-33B-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="webbrain-one/Nanbeige4.3-33B-NVFP4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("webbrain-one/Nanbeige4.3-33B-NVFP4")
model = AutoModelForCausalLM.from_pretrained("webbrain-one/Nanbeige4.3-33B-NVFP4", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use webbrain-one/Nanbeige4.3-33B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "webbrain-one/Nanbeige4.3-33B-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": "webbrain-one/Nanbeige4.3-33B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/webbrain-one/Nanbeige4.3-33B-NVFP4

SGLang

How to use webbrain-one/Nanbeige4.3-33B-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 "webbrain-one/Nanbeige4.3-33B-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": "webbrain-one/Nanbeige4.3-33B-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 "webbrain-one/Nanbeige4.3-33B-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": "webbrain-one/Nanbeige4.3-33B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use webbrain-one/Nanbeige4.3-33B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/webbrain-one/Nanbeige4.3-33B-NVFP4
```

webbrain-one commited on 4 days ago

Commit

db25813

verified ·

1 Parent(s): df94232

Add a short WebBrain note to the model card

Browse files

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README.md +2 -0

README.md CHANGED Viewed

@@ -27,6 +27,8 @@ format for vLLM.
 - **Kept in BF16:** token embeddings, `lm_head`, norms.
 - **Size:** 22.1 GB (BF16 original: 66.2 GB).
 ## Quick quality check
 | Model | GSM8K, first 100 test questions, greedy, non-thinking |

 - **Kept in BF16:** token embeddings, `lm_head`, norms.
 - **Size:** 22.1 GB (BF16 original: 66.2 GB).
+> Quantized by [WebBrain](https://webbrain.one), an open-source (MIT) AI browser agent for Chrome and Firefox that reads pages, extracts data and automates tasks. It runs on this model locally through the vLLM endpoint below.
 ## Quick quality check
 | Model | GSM8K, first 100 test questions, greedy, non-thinking |