Instructions to use ByteDance-Seed/Stable-DiffCoder-8B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ByteDance-Seed/Stable-DiffCoder-8B-Base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ByteDance-Seed/Stable-DiffCoder-8B-Base", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/Stable-DiffCoder-8B-Base", trust_remote_code=True)
model = AutoModel.from_pretrained("ByteDance-Seed/Stable-DiffCoder-8B-Base", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ByteDance-Seed/Stable-DiffCoder-8B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ByteDance-Seed/Stable-DiffCoder-8B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteDance-Seed/Stable-DiffCoder-8B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ByteDance-Seed/Stable-DiffCoder-8B-Base

SGLang

How to use ByteDance-Seed/Stable-DiffCoder-8B-Base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ByteDance-Seed/Stable-DiffCoder-8B-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteDance-Seed/Stable-DiffCoder-8B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ByteDance-Seed/Stable-DiffCoder-8B-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteDance-Seed/Stable-DiffCoder-8B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ByteDance-Seed/Stable-DiffCoder-8B-Base with Docker Model Runner:
```
docker model run hf.co/ByteDance-Seed/Stable-DiffCoder-8B-Base
```

Stable-DiffCoder-8B-Base

File size: 847 Bytes

9826ba0

 # Copyright (c) 2026 ByteDance Ltd. and/or its affiliates
 # SPDX-License-Identifier: MIT

from .generation_utils import generate_block
from transformers.models.llama.modeling_llama import LlamaForCausalLM
from transformers.generation.utils import GenerationConfig
import torch

class SeedDiffcoderForCausalLM(LlamaForCausalLM):
    @torch.no_grad()
    def generate(
        self,
        input_ids=None,
        generation_config: GenerationConfig = None,
        **kwargs,
    ):
        if input_ids is None:
            raise ValueError("input_ids must be provided")

        if generation_config is None:
            generation_config = self.generation_config

        prompt = input_ids
        output_ids, nfe = generate_block(
            model=self,
            prompt=prompt,
            **kwargs,
        )

        return output_ids