Stable-DiffCoder-8B-Instruct

Introduction

We are thrilled to introduce Stable-DiffCoder, which is a strong code diffusion large language model. Built directly on the Seed-Coder architecture, data, and training pipeline, it introduces a block diffusion continual pretraining (CPT) stage with a tailored warmup and block-wise clipped noise schedule.

Under identical architecture and data settings, we systematically analyze and design an efficient diffusion training pipeline that is not only stable but also potentially lifts the model’s performance ceiling. With this recipe, Stable-DiffCoder demonstrates overall performance improvements compared to its autoregressive (AR) counterpart across a broad set of code benchmarks, while any-order modeling improves structured code handling for editing and reasoning, and diffusion-based corruption aids learning for low-resource programming languages.

Notably, with only CPT followed by supervised fine-tuning, Stable-DiffCoder further surpasses many strong ∼8B AR and diffusion-based code models. These results demonstrate that diffusion-based training can improve code modeling quality beyond what AR training alone can achieve, even under tightly controlled data and architecture constraints.

This repo contains the Stable-DiffCoder-8B-Instruct model, which has the following features:

• Type: Mask Diffusion Language Models
• Training Stage: Pretraining & Post-training
• Data Source: Public datasets, synthetic data
• Context Length: 8192

Model Downloads

Model Name	Length	Download	Notes
Stable-DiffCoder-8B-Base	8K	🤗 Model	Pretrained on our model-centric code data.
👉 Stable-DiffCoder-8B-Instruct	8K	🤗 Model	Instruction-tuned for alignment with user intent.

Requirements

You need to install a specific version of transformers to adapt to the inference process:

pip install transformers==4.46.2

Explanation of Inference Parameters

• steps: Number of steps for diffusion generation
• gen_length: Maximum length of the generated output
• block_length: Length of the diffusion block, with a default value of 4
• temperature: Temperature for generation, with a default value of 0.0
• remasking: Remasking strategy, optional values are 'low_confidence' or 'random', default value is 'low_confidence' (for principle, refer to LLADA)
• tokenizer: Tokenizer used for text encoding and decoding
• shift: Whether to shift the output to the right by one position (similar to AutoRegressive/AR), default value is False
• threshold: Threshold for decoding (range: 0-1.0), default value is None; a smaller value results in faster decoding speed (for principle, refer to Fast-DLLM)
• eos_id: ID of the end-of-sequence token, default value is tokenizer.eos_token_id

Quickstart

Here is a simple example demonstrating how to load the model and generate code.

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

device = 'cuda'
model = AutoModelForCausalLM.from_pretrained('ByteDance-Seed/Stable-DiffCoder-8B-Instruct', trust_remote_code=True, torch_dtype=torch.bfloat16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained('ByteDance-Seed/Stable-DiffCoder-8B-Instruct', trust_remote_code=True)

prompt = 'Write a quick sort algorithm.'
m = [{"role": "user", "content": prompt}, ]
prompt = tokenizer.apply_chat_template(m, add_generation_prompt=True, tokenize=False)
input_ids = tokenizer(prompt)['input_ids']
input_ids = torch.tensor(input_ids).to(device).unsqueeze(0)

out = model.generate(input_ids, steps=512, gen_length=512, block_length=4, temperature=0., remasking='low_confidence', tokenizer=tokenizer, shift=False, threshold=None, eos_id=tokenizer.eos_token_id)
print(tokenizer.decode(out[0][input_ids.shape[1]:], skip_special_tokens=True))

Evaluation

Stable-DiffCoder-8B-Instruct has been evaluated on a wide range of coding tasks, including code generation, code reasoning, code editing, achieving stronger performance than a wide range of ∼8B ARs and DLLMs,

• Compared with ∼8B AR models：

Model	HumanEval	MBPP	MHPP	BigCodeBench (Full)	BigCodeBench (Hard)	LiveCodeBench (v5)
CodeLlama-7B-Instruct	40.9	54.0	6.7	25.7	4.1	3.6
DeepSeek-Coder-6.7B-Instruct	74.4	74.9	20.0	43.8	15.5	9.6
CodeQwen1.5-7B-Chat	83.5	77.7	17.6	43.6	15.5	3.0
Yi-Coder-9B-Chat	82.3	82.0	26.7	49.0	17.6	17.5
Llama-3.1-8B-Instruct	68.3	70.1	17.1	40.5	13.5	11.5
OpenCoder-8B-Instruct	83.5	79.1	30.5	50.9	18.9	17.1
Qwen2.5-Coder-7B-Instruct	88.4	83.5	26.7	48.8	20.3	17.3
Qwen3-8B	84.8	77.0	32.8	51.7	23.0	23.5
Seed-Coder-8B-Instruct	84.8	85.2	36.2	53.3	26.4	24.7
Stable-DiffCoder-8B-Instruct	86.6	85.7	42.4	54.8	31.8	23.5

• Compared with ∼8B DLLM models：

Model	HumanEval	HumanEval+	MBPP	MBPP+	BigCodeBench (Full)
LLaDA-8B-Instruct	49.4	-	41.0	-	16.5
Dream-7B-Instruct	63.4	-	68.3	-	10.6
LLaDA-MoE-7B-Instruct	61.6	-	70.0	-	20.4
Fast-dLLMv2	43.9	40.2	50.0	41.3	49.0
DiffuCoder-7B-Instruct	72.0	65.2	75.1	61.9	35.7
Dream-Coder-7B-Instruct	82.9	-	79.6	-	37.1
SDAR-8B-Chat	78.7	-	72.0	-	-
WeDLM-8B-Chat	80.5	73.8	70.5	-	-
Stable-DiffCoder-8B-Instruct	86.6	82.3	85.7	72.8	54.8

For detailed benchmark performance, please refer to our 📑 Technical Report.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{seed2026stablediffcoder,
      title={{Stable-DiffCoder}: Pushing the Frontier of Code Diffusion Large Language Model}, 
      author={{ByteDance Seed} and Chenghao Fan and Wen Heng and Bo Li and Sichen Liu and Yuxuan Song and Jing Su and Xiaoye Qu and Kai Shen and Wei Wei},
      year={2026}
}