If you’ve never imagined how diffusion models, commonly used in image generation, could revolutionize code generation, Apple’s DiffuCoder-7B-cpGRPO might just blow your mind. This cutting-edge diffusion-based large language model (dLLM) offers a radical alternative to traditional autoregressive (AR) code models like GPT. Unlike AR models that generate code token-by-token in a fixed order, DiffuCoder leverages global sequence-level planning and iterative denoising, allowing it to reason more holistically and creatively during generation. The newest cpGRPO variant is even more powerful, fine-tuned using a novel reinforcement learning method, coupled-GRPO, which not only boosts performance on real-world coding benchmarks by +4.4% on EvalPlus but also drastically reduces AR decoding bias. What makes DiffuCoder truly standout is its ability to flexibly generate code with diverse token orderings, enabling more robust search during training and more innovative completions at inference time.
In this guide, we’ll walk you through how to install and run Apple’s DiffuCoder locally or on the cloud.
Prerequisites
The minimum system requirements for running this model are:
- GPU: 1x RTX 4090 or 1x RTX A6000
- Storage: 20 GB (preferable)
- VRAM: at least 16 GB
- Anaconda installed
Step-by-step process to install and run DiffuCoder-7B-cpGRPO
For the purpose of this tutorial, we’ll use a GPU-powered Virtual Machine by NodeShift since it provides high compute Virtual Machines at a very affordable cost on a scale that meets GDPR, SOC2, and ISO27001 requirements. Also, it offers an intuitive and user-friendly interface, making it easier for beginners to get started with Cloud deployments. However, feel free to use any cloud provider of your choice and follow the same steps for the rest of the tutorial.
Step 1: Setting up a NodeShift Account
Visit app.nodeshift.com and create an account by filling in basic details, or continue signing up with your Google/GitHub account.
If you already have an account, login straight to your dashboard.
Step 2: Create a GPU Node
After accessing your account, you should see a dashboard (see image), now:
- Navigate to the menu on the left side.
- Click on the GPU Nodes option.
- Click on Start to start creating your very first GPU node.
These GPU nodes are GPU-powered virtual machines by NodeShift. These nodes are highly customizable and let you control different environmental configurations for GPUs ranging from H100s to A100s, CPUs, RAM, and storage, according to your needs.
Step 3: Selecting configuration for GPU (model, region, storage)
- For this tutorial, we’ll be using 1x RTX A6000 GPU, however, you can choose any GPU as per the prerequisites.
- Similarly, we’ll opt for 200GB storage by sliding the bar. You can also select the region where you want your GPU to reside from the available ones.
Step 4: Choose GPU Configuration and Authentication method
- After selecting your required configuration options, you’ll see the available GPU nodes in your region and according to (or very close to) your configuration. In our case, we’ll choose a 1x RTX A6000 48GB GPU node with 64vCPUs/63GB RAM/200GB SSD.
2. Next, you’ll need to select an authentication method. Two methods are available: Password and SSH Key. We recommend using SSH keys, as they are a more secure option. To create one, head over to our official documentation.
Step 5: Choose an Image
The final step is to choose an image for the VM, which in our case is Nvidia Cuda.
That’s it! You are now ready to deploy the node. Finalize the configuration summary, and if it looks good, click Create to deploy the node.
Step 6: Connect to active Compute Node using SSH
- As soon as you create the node, it will be deployed in a few seconds or a minute. Once deployed, you will see a status Running in green, meaning that our Compute node is ready to use!
- Once your GPU shows this status, navigate to the three dots on the right, click on Connect with SSH, and copy the SSH details that appear.
As you copy the details, follow the below steps to connect to the running GPU VM via SSH:
- Open your terminal, paste the SSH command, and run it.
2. In some cases, your terminal may take your consent before connecting. Enter ‘yes’.
3. A prompt will request a password. Type the SSH password, and you should be connected.
Output:
Next, If you want to check the GPU details, run the following command in the terminal:
!nvidia-smi
Step 7: Set up the project environment with dependencies
- Create a virtual environment using Anaconda.
conda create -n coder python=3.11 -y && conda activate coder
Output:
2. Once you’re inside the environment, install necessary dependencies to run the model.
pip install --upgrade vllm==0.8.4
pip install setuptools
Output:
3. Install and run jupyter notebook.
conda install -c conda-forge --override-channels notebook -y
conda install -c conda-forge --override-channels ipywidgets -y
jupyter notebook --allow-root
4. If you’re on a remote machine (e.g., NodeShift GPU), you’ll need to do SSH port forwarding in order to access the jupyter notebook session on your local browser.
Run the following command in your local terminal after replacing:
<YOUR_SERVER_PORT> with the PORT allotted to your remote server (For the NodeShift server – you can find it in the deployed GPU details on the dashboard).
<PATH_TO_SSH_KEY> with the path to the location where your SSH key is stored.
<YOUR_SERVER_IP> with the IP address of your remote server.
ssh -L 8888:localhost:8888 -p <YOUR_SERVER_PORT> -i <PATH_TO_SSH_KEY> root@<YOUR_SERVER_IP>
Output:
After this copy the URL you received in your remote server:
And paste this on your local browser to access the Jupyter Notebook session.
Step 8: Download and Run the model
- Open a Python notebook inside Jupyter.
2. Download the model checkpoints, load it to GPU and configure prompt and voice.
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])
Output:
Conclusion
By combining the innovative capabilities of Apple’s DiffuCoder-7B-cpGRPO with the ease and scalability of Nodeshift Cloud, you get the best of both worlds, state-of-the-art code generation powered by diffusion models, and hassle-free deployment in minutes. Whether you’re running on NodeShift cloud for experimentation or scaling with NodeShift GPUs locally on your own trusted bare metal servers for real-world applications, Nodeshift makes it seamless to spin up powerful GPU environments, manage dependencies, and focus purely on building with powerful models.
