Instructions to use tencent/Hy4-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tencent/Hy4-preview with Transformers:

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

pipe = pipeline("text-generation", model="tencent/Hy4-preview")
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("tencent/Hy4-preview")
model = AutoModelForCausalLM.from_pretrained("tencent/Hy4-preview", 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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tencent/Hy4-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tencent/Hy4-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tencent/Hy4-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tencent/Hy4-preview

SGLang

How to use tencent/Hy4-preview 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 "tencent/Hy4-preview" \
    --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": "tencent/Hy4-preview",
		"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 "tencent/Hy4-preview" \
        --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": "tencent/Hy4-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tencent/Hy4-preview with Docker Model Runner:
```
docker model run hf.co/tencent/Hy4-preview
```

Add LHTB 2.0 (Long-Horizon Terminal-Bench) eval result: 15/77 tasks solved

#14

by zli12321 - opened 1 day ago

base: refs/heads/main

←

from: refs/pr/14

Discussion Files changed

-0

zli12321

1 day ago

Adds the Long-Horizon Terminal-Bench 2.0 (LHTB 2.0) result for Hunyuan hy4 so it appears on the LHTB benchmark leaderboard under the lhtb2_solved task.

Score: 15 / 77 tasks solved at reward >= 0.95 (pass@0.95)

Metric	Value
Solved (reward >= 0.95)	15 / 77
Strict pass (reward = 1.0)	14 / 77
Mean reward (77 tasks)	0.532
Agent	terminus-2 (official LHTB Harbor harness)
Budget	3 hours per task, 1 trial per task

LHTB measures how well agents sustain useful work in a containerized terminal
over hundreds of steps, graded by hidden rebuild-from-artifact verifiers. LHTB 2.0
has 77 tasks: the original 46 plus 31 new ones, 30 of them reconstruction tasks.

Full results, with per-task rewards and trajectories linked from there:
https://github.com/zli12321/LHTB/tree/lhtb-2.0#lhtb-20-results-october-2026

Submitted by the LHTB maintainers; happy to adjust the formatting or withdraw if you prefer.

Add LHTB 2.0 (Long-Horizon Terminal-Bench) eval result: 15/77 tasks solved0f4b92e4

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