Instructions to use ByteDance-Seed/Seed-Coder-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ByteDance-Seed/Seed-Coder-8B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ByteDance-Seed/Seed-Coder-8B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Instruct") model = AutoModelForCausalLM.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ByteDance-Seed/Seed-Coder-8B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ByteDance-Seed/Seed-Coder-8B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ByteDance-Seed/Seed-Coder-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ByteDance-Seed/Seed-Coder-8B-Instruct
- SGLang
How to use ByteDance-Seed/Seed-Coder-8B-Instruct 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/Seed-Coder-8B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ByteDance-Seed/Seed-Coder-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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/Seed-Coder-8B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ByteDance-Seed/Seed-Coder-8B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ByteDance-Seed/Seed-Coder-8B-Instruct with Docker Model Runner:
docker model run hf.co/ByteDance-Seed/Seed-Coder-8B-Instruct
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| Model | HumanEval | MBPP | MHPP | BigCodeBench (Full) | BigCodeBench (Hard) | LiveCodeBench (2410 – 2502) |
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|:-----------------------------:|:---------:|:----:|:----:|:-------------------:|:-------------------:|:-------------------------:|
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| CodeLlama-7B-Instruct | 40.9 | 54.0 | 6.7 |
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| DeepSeek-Coder-6.7B-Instruct | 74.4 | 74.9 | 20.0 |
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| CodeQwen1.5-7B-Chat | 83.5 | 77.7 | 17.6 |
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| Yi-Coder-9B-Chat | 82.3 | 82.0 | 26.7 |
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| Llama-3.1-8B-Instruct | 68.3 | 70.1 | 17.1 |
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| OpenCoder-8B-Instruct | 83.5 | 79.1 | 30.5 |
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| Qwen2.5-Coder-7B-Instruct | 88.4 |
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| Qwen3-8B | 84.8 | 77.0 | 32.8 | 51.7 | 23.0 | 23.5 |
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| Seed-Coder-8B-Instruct | 84.8 | 85.2 | 36.2 | 53.3 |
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For detailed benchmark performance, please refer to our [📑 Technical Report](https://github.com/ByteDance-Seed/Seed-Coder/blob/master/Seed-Coder.pdf).
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| Model | HumanEval | MBPP | MHPP | BigCodeBench (Full) | BigCodeBench (Hard) | LiveCodeBench (2410 – 2502) |
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|:-----------------------------:|:---------:|:----:|:----:|:-------------------:|:-------------------:|:-------------------------:|
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| CodeLlama-7B-Instruct | 40.9 | 54.0 | 6.7 | 25.7 | 4.1 | 3.6 |
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| DeepSeek-Coder-6.7B-Instruct | 74.4 | 74.9 | 20.0 | 43.8 | 15.5 | 9.6 |
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| CodeQwen1.5-7B-Chat | 83.5 | 77.7 | 17.6 | 43.6 | 15.5 | 3.0 |
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| Yi-Coder-9B-Chat | 82.3 | 82.0 | 26.7 | 49.0 | 17.6 | 17.5 |
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| Llama-3.1-8B-Instruct | 68.3 | 70.1 | 17.1 | 40.5 | 13.5 | 11.5 |
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| OpenCoder-8B-Instruct | 83.5 | 79.1 | 30.5 | 50.9 | 18.9 | 17.1 |
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| Qwen2.5-Coder-7B-Instruct | **88.4** | 83.5 | 26.7 | 48.8 | 20.3 | 17.3 |
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| Qwen3-8B | 84.8 | 77.0 | 32.8 | 51.7 | 23.0 | 23.5 |
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| Seed-Coder-8B-Instruct | 84.8 | **85.2** | **36.2** | **53.3** | **26.4** | **24.7** |
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For detailed benchmark performance, please refer to our [📑 Technical Report](https://github.com/ByteDance-Seed/Seed-Coder/blob/master/Seed-Coder.pdf).
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