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---
license: unknown
base_model:
- apple/DiffuCoder-7B-Instruct
tags:
- code
- text-diffusion-model
- diffusion large language model
---
### DiffuCoder-7B-cpGRPO
The DiffuCoder-7B-cpGRPO variant further refines DiffuCoder-Instruct with reinforcement learning via Coupled-GRPO.
Training recipe:
- Initialized from DiffuCoder-7B-Instruct, post-training with coupled-GRPO on 21K code data (1 epoch).
- coupled-GRPO significantly improves DiffuCoder's performance on code generation benchmarks (+4.4\% on EvalPlus) and reduces reliance on AR bias during decoding.
#### More details and usage examples:
- Paper: [DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation](https://arxiv.org/abs/2506.20639)
- GitHub: https://github.com/apple/ml-diffucoder
```
import torch
from transformers import AutoModel, AutoTokenizer
model_path = "apple/DiffuCoder-7B-cpGRPO"
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()
query = "Write a function to find the shared elements from the given two lists."
prompt = f"""<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
{query.strip()}
<|im_end|>
<|im_start|>assistant
""" ## following the template of qwen; you can also use apply_chat_template function
TOKEN_PER_STEP = 1 # diffusion timesteps * TOKEN_PER_STEP = total new tokens
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs.input_ids.to(device="cuda")
attention_mask = inputs.attention_mask.to(device="cuda")
output = model.diffusion_generate(
input_ids,
attention_mask=attention_mask,
max_new_tokens=256,
output_history=True,
return_dict_in_generate=True,
steps=256//TOKEN_PER_STEP,
temperature=0.4,
top_p=0.95,
alg="entropy",
alg_temp=0.,
)
generations = [
tokenizer.decode(g[len(p) :].tolist())
for p, g in zip(input_ids, output.sequences)
]
print(generations[0].split('<|dlm_pad|>')[0])
```
#### Acknowledgement
To power this HuggingFace model release, we reuse [Dream](https://huggingface.co/Dream-org/Dream-v0-Base-7B)'s modeling architecture and generation utils. |