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
```

llama cpp

by moon005 - opened Dec 30, 2025

Discussion

moon005

Dec 30, 2025

now only if someone does convert this to gguf? it will be so awesome to run it with llama cpp.

krustik

Dec 30, 2025

thats only when it will be added to list by llama-cpp. I see by size that it could fit inside GPU, maybe Q8 quality in 16Gb VRAM, and almost original BF16 will fit in 24Gb VRAM cards.
It can be used as webbrowser companion, kinda only Brave supports local models (transmitted by Ollama or oobabooga or etc). I don't see the way to make same functionality in original non-GGUF format.

exlaw

Tencent org Jan 1

Hi, because our method is non-autoregressive, it doesn't work with llama.cpp out of the box and needs some extra dev work. The good news is that since we use pure causal attention, the inference logic is doable. It's on our roadmap, but please give us a little time.

However, our checkpoints do support AR generation. So right now, if you convert the model directly, it will only work in the standard AR mode.

moon005 changed discussion status to closed Jan 1

moon005 changed discussion status to open Jan 1

moon005

Jan 1

Hi, because our method is non-autoregressive, it doesn't work with llama.cpp out of the box and needs some extra dev work. The good news is that since we use pure causal attention, the inference logic is doable. It's on our roadmap, but please give us a little time.

However, our checkpoints do support AR generation. So right now, if you convert the model directly, it will only work in the standard AR mode.

amazing work on this model. would be definitely waiting for more development on this. out of the box this is already incredible. :)

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment