Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

Seed-Coder-8B-Instruct

Introduction

We are thrilled to introduce Seed-Coder, a powerful, transparent, and parameter-efficient family of open-source code models at the 8B scale, featuring base, instruct, and reasoning variants. Seed-Coder contributes to promote the evolution of open code models through the following highlights.

• Model-centric: Seed-Coder predominantly leverages LLMs instead of hand-crafted rules for code data filtering, minimizing manual effort in pretraining data construction.
• Transparent: We openly share detailed insights into our model-centric data pipeline, including methods for curating GitHub data, commits data, and code-related web data.
• Powerful: Seed-Coder achieves state-of-the-art performance among open-source models of comparable size across a diverse range of coding tasks.

This repo contains the Seed-Coder-8B-Instruct model, which has the following features:

• Type: Causal language models
• Training Stage: Pretraining & Post-training
• Data Source: Public datasets, synthetic data
• Context Length: 32,768

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	64K	🤗 Model	RL trained to boost reasoning capabilities.
Seed-Coder-8B-Reasoning-bf16	64K	🤗 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 has been evaluated on a wide range of coding tasks, including code generation, code reasoning, code editing, and software engineering, achieving state-of-the-art performance among ~8B open-source models.

Model	HumanEval	MBPP	MHPP	BigCodeBench (Full)	BigCodeBench (Hard)	LiveCodeBench (2410 – 2502)
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

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.