Instructions to use tiny-random/hy4-preview-fp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/hy4-preview-fp8 with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/hy4-preview-fp8")
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("tiny-random/hy4-preview-fp8")
model = AutoModelForCausalLM.from_pretrained("tiny-random/hy4-preview-fp8", 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 tiny-random/hy4-preview-fp8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/hy4-preview-fp8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/hy4-preview-fp8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiny-random/hy4-preview-fp8

SGLang

How to use tiny-random/hy4-preview-fp8 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 "tiny-random/hy4-preview-fp8" \
    --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": "tiny-random/hy4-preview-fp8",
		"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 "tiny-random/hy4-preview-fp8" \
        --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": "tiny-random/hy4-preview-fp8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/hy4-preview-fp8 with Docker Model Runner:
```
docker model run hf.co/tiny-random/hy4-preview-fp8
```

yujiepan commited on 2 days ago

Commit

b074511

verified ·

1 Parent(s): 207756b

Clarify validated GPU configurations

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -33,7 +33,7 @@ expert intermediate partition, hence a 512-wide full expert intermediate size.
 Checkpoint precision matches the source ModelOpt MXFP8 config. Marlin uses BF16
 activations at runtime (W8A16).
-Transformers and vLLM TP=4 were validated on NVIDIA H20 with English, Chinese and
 long DSA prompts. The vLLM run enabled three-token MTP and CUDA Graph. Its separate
 test environment used vLLM 0.32.0, Torch 2.13.0+cu130, CUDA 13.0, Transformers 5.19.0
 and FlashInfer 0.7.0.post1; the Transformers/SGLang environment is listed below.

 Checkpoint precision matches the source ModelOpt MXFP8 config. Marlin uses BF16
 activations at runtime (W8A16).
+Transformers on one GPU and vLLM at TP=4 were validated on NVIDIA H20 with English, Chinese and
 long DSA prompts. The vLLM run enabled three-token MTP and CUDA Graph. Its separate
 test environment used vLLM 0.32.0, Torch 2.13.0+cu130, CUDA 13.0, Transformers 5.19.0
 and FlashInfer 0.7.0.post1; the Transformers/SGLang environment is listed below.