DiffuCoder-7B-Instruct

A new paradigm in code generation developed by Apple. Bringing the iterative refinement capabilities of Diffusion Models to coding assistance, breaking the limitations of traditional linear generation.

Apple Research Diffusion LLM Open Source 7B Parameters

Core Principle: Diffusion vs. Autoregressive

DiffuCoder's core innovation is that it is Non-Autoregressive. Unlike traditional GPT-style models that generate word-by-word from left to right, DiffuCoder handles code like an image: starting from complete "noise", it globally iterates to gradually "denoise" into a clear code structure.

Traditional LLM (Autoregressive)

DiffuCoder (Diffusion)

* Left: Linear thinking. Once a mistake is made early on, it's hard to correct.
* Right: Global thinking. The model sees the "whole picture" and refines details iteratively.

Model Lineage & Training Recipe

Lineage

Ancestor: Qwen2.5-7B
Code Adaptation: Qwen2.5-Coder-7B
Current Model: DiffuCoder-7B-Instruct

Training Recipe

Masked Diffusion: Treats code generation as a fill-in-the-blank and denoising process.
OpenCoder-SFT: Instruction tuning using second-stage datasets (5 epochs).
Special Tokens: Introduced Pad Token to handle fixed-length sequences and ensure structural stability.

Performance Benchmarks

Despite its non-traditional generation method, DiffuCoder-7B-Instruct shows surprisingly competitive performance in code benchmarks, proving the potential of diffusion models for discrete data.

Benchmark	DiffuCoder-7B-Instruct	Base Model	Improvement
HumanEval (Python)	78.8%	64.3%	+14.5%
EvalPlus	~55.4%	N/A	Strong

Source: arXiv:2506.20639 & Community Benchmarks

How to Use

The model can be loaded via Hugging Face Transformers. Note the core method is diffusion_generate.

import torch
from transformers import AutoModel, AutoTokenizer

# 1. Load Model & Tokenizer
model_path = "apple/DiffuCoder-7B-Instruct"
model = AutoModel.from_pretrained(
    model_path, 
    torch_dtype=torch.bfloat16, 
    trust_remote_code=True
).to("cuda").eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

# 2. Build Prompt (Qwen Style)
query = "Write a Python function to merge two sorted lists."
# Note: Using HTML entity escapes < and { to avoid Svelte parsing errors
prompt = f"<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"

# 3. Diffusion Generation Process
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.diffusion_generate(
    inputs.input_ids,
    attention_mask=inputs.attention_mask,
    max_new_tokens=256,
    steps=128,          # Number of denoising steps
    temperature=0.3,    # Sampling temperature
    top_p=0.95
)

# 4. Decode Output
print(tokenizer.decode(output.sequences[0], skip_special_tokens=True))

Frequently Asked Questions (FAQ)

Q: Why use diffusion models for code generation?

Traditional Autoregressive (AR) models predict the next token sequentially, making it hard to "plan ahead" or "look back". Diffusion models generate a fuzzy outline of the code first (like signatures and logic) and then fill in the details. This mechanism can be more robust for handling complex, long-range dependencies.

Q: Can I run this on my MacBook?

Yes. Although it is a 7B (effectively ~8B) parameter model, it is based on the Transformer architecture and can be quantized (e.g., 4-bit) using the MLX framework. The community has successfully tested quantized versions on Apple Silicon with decent inference speeds.

Q: How is the generation speed compared to traditional LLMs?

Diffusion inference is typically slower than a single-pass AR model because it requires multiple timesteps to denoise. However, since it is non-autoregressive, it can theoretically benefit from parallel computation. You can trade off quality for speed by adjusting the `steps` parameter.

Q: Is it allowed for commercial use?

The model uses the custom apple-amlr license. While it is open source, it is recommended to read the LICENSE file in the repository carefully before using it commercially.

Based on arXiv:2506.20639 research paper & GitHub repository data.