metadata
license: mit
base_model:
- ByteDance-Seed/Seed-Coder-8B-Base
Seed-Coder-8B-Instruct
Introduction
Seed-Coder-8B-Instruct is an 8-billion-parameter model instruction-tuned specifically for code generation, code reasoning, and code understanding. It is built to empower developers with high-quality, efficient code assistance. It features:
- Trained on a massively curated corpus, where an LLM-based filter is applied to select high-quality real-world code, text-code alignment data, and synthetic datasets — ensuring cleaner and more useful data compared to traditional heuristic-based curation.
- Achieves superior performance across code generation, bug fixing, and reasoning tasks, rivaling or surpassing larger open-source code models.
- Instruction-tuned to reliably follow user intents across a diverse range of coding and reasoning prompts.
- Supports long-context handling up to 32K tokens, enabling processing of complex multi-file projects and detailed coding tasks.
Model Downloads
| Model Name | Length | Download | Notes |
|---|---|---|---|
| Seed-Coder-8B-Base | 32K | 🤗 Model | Pretrained on our model-centric code data. |
| 👉 Seed-Coder-8B-Instruct | 32K | 🤗 Model | Instruction-tuned for alignment with user intent. |
| Seed-Coder-8B-Reasoning | 32K | 🤗 Model | RL trained to boost reasoning capabilities. |
Requirements
You will need to install the latest versions of transformers and accelerate:
pip install -U transformers accelerate
Quickstart
Here is a simple example demonstrating how to load the model and generate code using the Hugging Face pipeline API:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "ByteDance-Seed/Seed-Coder-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
messages = [
{"role": "user", "content": "Write a quick sort algorithm."},
]
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
return_tensors="pt",
add_generation_prompt=True,
).to(model.device)
outputs = model.generate(input_ids, max_new_tokens=512)
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)
Evaluation
Seed-Coder-8B-Instruct demonstrates strong performance across a variety of coding benchmarks, showing:
- Competitive or superior results compared to similarly sized open-source code models.
- Robustness across different programming languages and domains.
- Ability to understand, reason, and repair complex code snippets.
| Model | HumanEval | MBPP | MHPP | BigCodeBench (Full) | BigCodeBench (Hard) | LiveCodeBench (2410 – 2502) |
|---|---|---|---|---|---|---|
| CodeLlama-7B-Instruct | 40.9 | 54.0 | 6.7 | 21.9 | 3.4 | 3.6 |
| DeepSeek-Coder-6.7B-Instruct | 74.4 | 74.9 | 20.0 | 35.5 | 10.1 | 9.6 |
| CodeQwen1.5-7B-Chat | 83.5 | 77.7 | 17.6 | 39.6 | 18.9 | 3.0 |
| Yi-Coder-9B-Chat | 82.3 | 82.0 | 26.7 | 38.1 | 11.5 | 17.5 |
| Llama-3.1-8B-Instruct | 68.3 | 70.1 | 17.1 | 36.6 | 13.5 | 11.5 |
| OpenCoder-8B-Instruct | 83.5 | 79.1 | 30.5 | 40.3 | 16.9 | 17.1 |
| Qwen2.5-Coder-7B-Instruct | 88.4 | 82.0 | 26.7 | 41.0 | 18.2 | 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 | 20.5 | 24.7 |
For detailed benchmark performance, please refer to our 📑 technical report.