Instructions to use renhouxing/ME-DLM-Stage1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use renhouxing/ME-DLM-Stage1 with Transformers:

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

pipe = pipeline("text-generation", model="renhouxing/ME-DLM-Stage1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("renhouxing/ME-DLM-Stage1", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use renhouxing/ME-DLM-Stage1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "renhouxing/ME-DLM-Stage1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "renhouxing/ME-DLM-Stage1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/renhouxing/ME-DLM-Stage1

SGLang

How to use renhouxing/ME-DLM-Stage1 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 "renhouxing/ME-DLM-Stage1" \
    --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": "renhouxing/ME-DLM-Stage1",
		"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 "renhouxing/ME-DLM-Stage1" \
        --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": "renhouxing/ME-DLM-Stage1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use renhouxing/ME-DLM-Stage1 with Docker Model Runner:
```
docker model run hf.co/renhouxing/ME-DLM-Stage1
```

Improve model card: add library_name and paper link

by nielsr HF Staff - opened 27 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-3

Files changed (1) hide show

README.md +5 -3

README.md CHANGED Viewed

@@ -1,14 +1,16 @@
 ---
-language:
-- en
 base_model:
 - GSAI-ML/LLaDA-8B-Base
 pipeline_tag: text-generation
 ---
 ## Edit-Based Refinement for Parallel Masked Diffusion Language Models
 <p align="center">
-    <a href="https://arxiv.org/abs/2605.09603">📄 Paper</a> •
     <a href="https://github.com/renhouxing/ME-DLM">🏠 Repo</a> •
     <a href="https://huggingface.co/renhouxing/ME-DLM-Stage3">🤖 Models</a>
 </p>

 ---
 base_model:
 - GSAI-ML/LLaDA-8B-Base
+language:
+- en
 pipeline_tag: text-generation
+library_name: transformers
 ---
 ## Edit-Based Refinement for Parallel Masked Diffusion Language Models
 <p align="center">
+    <a href="https://huggingface.co/papers/2605.09603">📄 Paper</a> •
     <a href="https://github.com/renhouxing/ME-DLM">🏠 Repo</a> •
     <a href="https://huggingface.co/renhouxing/ME-DLM-Stage3">🤖 Models</a>
 </p>