Instructions to use Kwai-Klear/Klear-46B-A2.5B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kwai-Klear/Klear-46B-A2.5B-Base with Transformers:

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

pipe = pipeline("text-generation", model="Kwai-Klear/Klear-46B-A2.5B-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Kwai-Klear/Klear-46B-A2.5B-Base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kwai-Klear/Klear-46B-A2.5B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kwai-Klear/Klear-46B-A2.5B-Base"
# 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/Klear-46B-A2.5B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kwai-Klear/Klear-46B-A2.5B-Base

SGLang

How to use Kwai-Klear/Klear-46B-A2.5B-Base 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/Klear-46B-A2.5B-Base" \
    --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/Klear-46B-A2.5B-Base",
		"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/Klear-46B-A2.5B-Base" \
        --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/Klear-46B-A2.5B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kwai-Klear/Klear-46B-A2.5B-Base with Docker Model Runner:
```
docker model run hf.co/Kwai-Klear/Klear-46B-A2.5B-Base
```

BubbleQ commited on Sep 5, 2025

Commit

7ea5b4c

verified ·

1 Parent(s): bb32577

Update configuration_klear.py

Browse files

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configuration_klear.py +1 -1

configuration_klear.py CHANGED Viewed

@@ -1,5 +1,5 @@
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-#           This file was automatically generated from src/transformers/models/Klear/modular_Klear.py.
 #               Do NOT edit this file manually as any edits will be overwritten by the generation of
 #             the file from the modular. If any change should be done, please apply the change to the
 #                          modular_Klear.py file directly. One of our CI enforces this.

 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/klear/modular_klear.py.
 #               Do NOT edit this file manually as any edits will be overwritten by the generation of
 #             the file from the modular. If any change should be done, please apply the change to the
 #                          modular_Klear.py file directly. One of our CI enforces this.