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
```

open-dcoder-0.5B / README.md

nielsr HF Staff

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	---
	language:
	- code
	library_name: transformers
	license: apache-2.0
	pipeline_tag: text-generation
	tags:
	- masked-diffusion
	- code-generation
	- qwen2
	---

	## 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.

	## How to Use

	First, make sure you have the latest `transformers` library installed.

	```bash
	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

	model_id = "fredzzp/open-dcoder-0.5B"
	device = "cuda" if torch.cuda.is_available() else "cpu"

	# trust_remote_code=True is essential
	tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	trust_remote_code=True
	).to(device)

	prompt = "def fibonacci(n):"
	input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)

	# The model will use the generation_config.json from the repo by default
	# You can also override parameters here
	outputs = model.diffusion_generate(
	inputs=input_ids,
	max_new_tokens=100,
	steps=16,
	temperature=0.8
	)

	# Decode the output
	prompt_len = input_ids.shape[1]
	generated_text = tokenizer.decode(outputs.sequences[0][prompt_len:], skip_special_tokens=True)

	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}}
	}
	```