Instructions to use Kwai-Klear/GoLongRL-30B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kwai-Klear/GoLongRL-30B-A3B with Transformers:

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

pipe = pipeline("text-generation", model="Kwai-Klear/GoLongRL-30B-A3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kwai-Klear/GoLongRL-30B-A3B")
model = AutoModelForCausalLM.from_pretrained("Kwai-Klear/GoLongRL-30B-A3B")
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 Kwai-Klear/GoLongRL-30B-A3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kwai-Klear/GoLongRL-30B-A3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kwai-Klear/GoLongRL-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kwai-Klear/GoLongRL-30B-A3B

SGLang

How to use Kwai-Klear/GoLongRL-30B-A3B 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 "Kwai-Klear/GoLongRL-30B-A3B" \
    --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": "Kwai-Klear/GoLongRL-30B-A3B",
		"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 "Kwai-Klear/GoLongRL-30B-A3B" \
        --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": "Kwai-Klear/GoLongRL-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kwai-Klear/GoLongRL-30B-A3B with Docker Model Runner:
```
docker model run hf.co/Kwai-Klear/GoLongRL-30B-A3B
```

Improve model card metadata and links

by nielsr HF Staff - opened 7 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+17

-11

nielsr

7 days ago

Hi! I'm Niels, part of the community science team at Hugging Face.

This PR improves the model card for GoLongRL-30B-A3B by:

Adding library_name: transformers and pipeline_tag: text-generation to the metadata for better discoverability and to enable code snippets.
Adding the datasets tag to link the model to its training data.
Updating the resource table to include the official GitHub repository and correctly linking the arXiv and Hugging Face paper pages.
Ensuring the overview image points to a valid resolution URL.

Improve model card metadata and links21303023

Suu changed pull request status to merged 6 days ago

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