Instructions to use tencent/WeDLM-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tencent/WeDLM-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="tencent/WeDLM-8B-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("tencent/WeDLM-8B-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tencent/WeDLM-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tencent/WeDLM-8B-Instruct"
# 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/WeDLM-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tencent/WeDLM-8B-Instruct

SGLang

How to use tencent/WeDLM-8B-Instruct 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/WeDLM-8B-Instruct" \
    --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/WeDLM-8B-Instruct",
		"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/WeDLM-8B-Instruct" \
        --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/WeDLM-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tencent/WeDLM-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/tencent/WeDLM-8B-Instruct
```

Is this model initialized from Qwen3-8B?

by JosephusCheung - opened Dec 29, 2025

Discussion

JosephusCheung

Dec 29, 2025

Hello,

I noticed that the model architecture and parameters appear to be structurally identical to Qwen3-8B.

Could you please clarify if this model was initialized from Qwen3 weights? Currently, the model card only mentions Qwen3 in the context of benchmarks, but lists "WeDLM-8B" as the base. If this is indeed built on top of Qwen3, it would be helpful to see that explicitly mentioned.

Thanks!

exlaw

Tencent org Dec 29, 2025

Yes, it is based on Qwen3-8B. WeDLM-8B-Base(https://huggingface.co/tencent/WeDLM-8B-Base) is from Qwen3-8B-Base. And WeDLM-8B-Instruct is based on WeDLM-8B-Base with further post-training.

exlaw

Tencent org Dec 29, 2025

Thanks for your suggestion. I have updated the readme.

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