Seed Coder 8B Base

🚀 Seed-Coder-8B-Base

Seed-Coder is a powerful, transparent, and parameter - efficient family of open - source code models at the 8B scale. It includes base, instruct, and reasoning variants, aiming to promote the evolution of open code models.

✨ Features

• Model - centric: Seed - Coder mainly uses LLMs for code data filtering instead of hand - crafted rules, reducing manual effort in pretraining data construction.
• Transparent: It openly shares detailed insights into the 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 various coding tasks.

📦 Installation

You will need to install the latest versions of transformers and accelerate:

pip install -U transformers accelerate

🚀 Quick Start

Here is a simple example demonstrating how to load the model and perform code generation using the Hugging Face pipeline API:

import transformers
import torch

model_id = "ByteDance-Seed/Seed-Coder-8B-Base"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

output = pipeline("def say_hello_world():", max_new_tokens=100)
print(output[0]["generated_text"])

💻 Usage Examples

Basic Usage

import transformers
import torch

model_id = "ByteDance-Seed/Seed-Coder-8B-Base"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

output = pipeline("def say_hello_world():", max_new_tokens=100)
print(output[0]["generated_text"])

Advanced Usage

Seed - Coder - 8B - Base natively supports Fill - in - the - Middle (FIM) tasks, where the model is given a prefix and a suffix and asked to predict the missing middle content. This allows for code infilling scenarios such as completing a function body or inserting missing logic between two pieces of code.

import transformers
import torch

model_id = "ByteDance-Seed/Seed-Coder-8B-Base"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

# You can concatenate a prefix, a special FIM separator token, and a suffix
prefix = "def add_numbers(a, b):\n    "
suffix = "\n    return result"

# Combine prefix and suffix following the FIM format
fim_input = '<[fim-suffix]>' + suffix + '<[fim-prefix]>' + prefix + '<[fim-middle]>'

output = pipeline(fim_input, max_new_tokens=512)
print(output[0]["generated_text"])

📚 Documentation

Model Information

Model Downloads

Evaluation

Seed - Coder - 8B - Base has been evaluated on code generation, code completion, and code reasoning benchmarks, achieving state - of - the - art performance among ~ 8B open - source models.

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.