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
```

xiaoxuanzi commited on 4 days ago

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@@ -26,6 +26,7 @@ We present GoLongRL, a fully open-source, capability-oriented post-training reci
 | Model | Avg. | DocMath | LBV2 | Frames | MRCR | CorpusQA | LBV1-QA |
 |---|---|---|---|---|---|---|---|
 | DeepSeek-R1-0528 | 68.7 | 63.4 | 59.5 | 76.9 | 64.9 | 77.5 | 69.9 |
 | Qwen3-235B-A22B-Thinking | 68.5 | 65.8 | 57.5 | 75.1 | 66.2 | 75.3 | 70.9 |
 | Gemini-2.5-Flash-Thinking | 68.7 | 64.8 | 56.8 | 65.8 | 78.8 | 79.4 | 66.9 |

 | Model | Avg. | DocMath | LBV2 | Frames | MRCR | CorpusQA | LBV1-QA |
 |---|---|---|---|---|---|---|---|
+| Qwen3-30B-A3B-Thinking-2507 | 60.1 | 63.3 | 48.7 | 70.2 | 41.6 | 70.5 | 66.5|
 | DeepSeek-R1-0528 | 68.7 | 63.4 | 59.5 | 76.9 | 64.9 | 77.5 | 69.9 |
 | Qwen3-235B-A22B-Thinking | 68.5 | 65.8 | 57.5 | 75.1 | 66.2 | 75.3 | 70.9 |
 | Gemini-2.5-Flash-Thinking | 68.7 | 64.8 | 56.8 | 65.8 | 78.8 | 79.4 | 66.9 |