Instructions to use fredzzp/open-dcoder-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fredzzp/open-dcoder-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="fredzzp/open-dcoder-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fredzzp/open-dcoder-0.5B")
model = AutoModelForCausalLM.from_pretrained("fredzzp/open-dcoder-0.5B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use fredzzp/open-dcoder-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fredzzp/open-dcoder-0.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fredzzp/open-dcoder-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fredzzp/open-dcoder-0.5B

SGLang

How to use fredzzp/open-dcoder-0.5B 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 "fredzzp/open-dcoder-0.5B" \
    --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": "fredzzp/open-dcoder-0.5B",
		"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 "fredzzp/open-dcoder-0.5B" \
        --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": "fredzzp/open-dcoder-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fredzzp/open-dcoder-0.5B with Docker Model Runner:
```
docker model run hf.co/fredzzp/open-dcoder-0.5B
```

nielsr HF Staff commited on 14 days ago

Commit

c678e54

verified ·

1 Parent(s): d0d86d5

Add paper link and improve model card metadata

Browse files

Hi, I'm Niels from the Hugging Face community science team. I'm opening this PR to improve your model card with relevant metadata and links to the associated research.

Specifically, I have:
- Added `pipeline_tag: text-generation` to the YAML metadata to improve discoverability on the Hub.
- Added a link to the paper: "Don't Retrain, Align: Adapting Autoregressive LMs to Diffusion LMs via Representation Alignment".
- Added a link to the official GitHub repository for easier access to training and evaluation scripts.
- Included the citation for the project.

Please feel free to review and merge if this looks good to you!

Files changed (1) hide show

README.md +22 -3

README.md CHANGED Viewed

@@ -1,8 +1,9 @@
 ---
-license: apache-2.0
 language:
 - code
 library_name: transformers
 tags:
 - masked-diffusion
 - code-generation
@@ -11,7 +12,12 @@ tags:
 ## Open Diffusion Large Language Models for Code Generation
-This repository contains the weights and custom code for the **fredzzp/open-dcoder-0.5B** model, a masked diffusion model for code generation based on the Qwen2 architecture.
 This model uses bidirectional attention and must be used with the custom `diffusion_generate` method.
@@ -23,8 +29,8 @@ First, make sure you have the latest `transformers` library installed.
 pip install transformers torch huggingface_hub
 ```
-You can then use the model for generation. Note: You must pass trust_remote_code=True to load the custom model architecture.
 ```python
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
@@ -59,3 +65,16 @@ generated_text = tokenizer.decode(outputs.sequences[0][prompt_len:], skip_specia
 print("--- Generated Code ---")
 print(generated_text)
 ```

 ---
 language:
 - code
 library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
 tags:
 - masked-diffusion
 - code-generation
 ## Open Diffusion Large Language Models for Code Generation
+This repository contains the weights and custom code for the **fredzzp/open-dcoder-0.5B** model, a masked diffusion model for code generation based on the Qwen2 architecture.
+The model was introduced in the paper [Don't Retrain, Align: Adapting Autoregressive LMs to Diffusion LMs via Representation Alignment](https://huggingface.co/papers/2605.06885).
+- **Code:** [pengzhangzhi/Open-dLLM](https://github.com/pengzhangzhi/Open-dLLM)
+- **Blog:** [Notion Blog](https://oval-shell-31c.notion.site/Open-Diffusion-Large-Language-Model-25e03bf6136480b7a4ebe3d53be9f68a?pvs=74)
 This model uses bidirectional attention and must be used with the custom `diffusion_generate` method.
 pip install transformers torch huggingface_hub
 ```
+You can then use the model for generation. Note: You must pass `trust_remote_code=True` to load the custom model architecture.
 ```python
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 print("--- Generated Code ---")
 print(generated_text)
 ```
+## Citation
+```bibtex
+@misc{opendllm2025,
+  title        = {Open-dLLM: Open Diffusion Large Language Models},
+  author       = {Fred Zhangzhi Peng, Shuibai Zhang, Alex Tong, and contributors},
+  year         = {2025},
+  howpublished = {\url{https://github.com/pengzhangzhi/Open-dLLM}},
+  note         = {Blog: \url{https://oval-shell-31c.notion.site/Open-Diffusion-Large-Language-Model-25e03bf6136480b7a4ebe3d53be9f68a?pvs=74},
+                  Model: \url{https://huggingface.co/fredzzp/open-dcoder-0.5B}}
+}
+```