apple / CADD-Base-7B

CADD-Base-7B

CADD-Base-7B is a masked diffusion language model for code generation, augmented with Continuously Augmented Discrete Diffusion (CADD) --- a continuous flow-matching signal that guides the discrete denoising process.

Key idea: At each diffusion step, a continuous embedding z_continuous is added to masked-token embeddings, following a linear flow-matching trajectory from noise to clean embeddings. This is orthogonal to the discrete unmasking strategy --- any MDM algorithm can be combined with CADD.

Usage

import torch
from transformers import AutoModel, AutoTokenizer

model_path = "apple/CADD-Base-7B"
model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = model.to("cuda").eval()

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

output = model.diffusion_generate(
    input_ids,
    max_new_tokens=512,
    steps=512,
    temperature=0.1,
    alg="entropy",
    alg_temp=0.0,
    use_cadd=True,
    cadd_sampling_mode="weighted",
)

print(tokenizer.decode(output[0], skip_special_tokens=True))

CADD Sampling Parameters

More details:

• Paper: Continuously Augmented Discrete Diffusion Model for Categorical Generative Modeling (ICLR 2026)
• GitHub: https://github.com/apple/ml-CADD

Citation

@article{zheng2025continuously,
  title={Continuously augmented discrete diffusion model for categorical generative modeling},
  author={Zheng, Huangjie and Gong, Shansan and Zhang, Ruixiang and Chen, Tianrong and Gu, Jiatao and Zhou, Mingyuan and Jaitly, Navdeep and Zhang, Yizhe},
  journal={arXiv preprint arXiv:2510.01329},
  year={2025}
}

Acknowledgment

To power this HuggingFace model release, we build upon and improve DiffuCoder , reusing Dream 's modeling architecture and generation utils.