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.
How to Use
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.
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)