Instructions to use 0xSero/Hy3-preview-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xSero/Hy3-preview-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="0xSero/Hy3-preview-NVFP4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("0xSero/Hy3-preview-NVFP4")
model = AutoModelForCausalLM.from_pretrained("0xSero/Hy3-preview-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 0xSero/Hy3-preview-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xSero/Hy3-preview-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": "0xSero/Hy3-preview-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0xSero/Hy3-preview-NVFP4

SGLang

How to use 0xSero/Hy3-preview-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 "0xSero/Hy3-preview-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": "0xSero/Hy3-preview-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 "0xSero/Hy3-preview-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": "0xSero/Hy3-preview-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 0xSero/Hy3-preview-NVFP4 with Docker Model Runner:
```
docker model run hf.co/0xSero/Hy3-preview-NVFP4
```

Hy3-preview NVFP4A16

This is a checkpoint-only NVFP4A16 quantization of tencent/Hy3-preview, produced with llmcompressor.entrypoints.model_free.model_free_ptq.

Base model: tencent/Hy3-preview
Quantization scheme: NVFP4A16
Ignored modules/patterns: lm_head, model.embed_tokens, re:.*router.gate$, re:.*expert_bias$
Source snapshot: recorded in QUANTIZATION_MANIFEST.json
License: inherits Tencent Hy Community License Agreement from the base model; original LICENSE is included.

Notes

This release quantizes safetensors weights without importing the custom HYV3 model class. Router gates, expert bias tensors, embeddings, and lm_head are preserved unquantized for compatibility/conservatism.

Downloads last month: 37

Safetensors

Model size

169B params

Tensor type

F32

BF16

F8_E4M3

Model tree for 0xSero/Hy3-preview-NVFP4

Base model

tencent/Hy3-preview

Quantized

(10)

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