Seed-Coder-8B-Reasoning Open-Source Code Model - Enhance Reasoning Ability and Handle Programming Tasks with Ultra-Long Context

Seed Coder 8B Reasoning

Developed by ByteDance-Seed

Seed-Coder-8B-Reasoning is an 8B-scale open-source code model with enhanced reasoning capabilities through reinforcement learning, supporting a context length of 65,536 and excelling in programming tasks.

Large Language Model

Transformers

Open Source License:MIT #64K Long-Context Inference #Competitive Programming Optimization #Reinforcement Learning Enhancement

Downloads 4,622

Release Time : 4/27/2025

Model Overview

Seed-Coder-8B-Reasoning is a variant of the Seed-Coder series, focusing on improving code generation and reasoning abilities. It reduces manual intervention through a model-centric approach, provides highly transparent data processing workflows, and achieves state-of-the-art performance in various programming tasks.

Model Features

Model-Centric Approach

Primarily utilizes large language models rather than manual rules for code data filtering, significantly reducing human effort in pre-training data construction.

High Transparency

Publicly shares complete details of the model-centric data pipeline, including processing methods for GitHub data, commit data, and web code data.

Strong Performance

Among open-source models of similar scale, Seed-Coder achieves state-of-the-art performance across various programming tasks.

Long-Context Support

Supports a context length of 65,536, making it suitable for handling complex programming tasks.

Model Capabilities

Code Generation

Code Completion

Algorithm Implementation

Programming Problem Solving

Complex Reasoning

Use Cases

Programming Education

Algorithm Teaching

Used to generate and explain various algorithms, such as quicksort and dynamic programming.

Capable of generating efficient and correct algorithm implementations.

Software Development

Code Generation

Generates code snippets or complete functions based on natural language descriptions.

Generated code exhibits high accuracy and readability.

Competitive Programming

Problem-Solving Assistance

Helps solve competitive programming problems, such as IOI and Codeforces challenges.

Outperformed QwQ-32B and DeepSeek-R1 in IOI'2024 and achieved ELO ratings comparable to o1-mini in Codeforces competitions.

🚀 Seed-Coder-8B-Reasoning

Seed-Coder-8B-Reasoning is a powerful, transparent, and parameter - efficient open - source code model at the 8B scale. It offers base, instruct, and reasoning variants, and shows excellent performance in various coding tasks.

🚀 Quick Start

To get started with the Seed - Coder - 8B - Reasoning model, you first need to install the necessary libraries. Then, you can use the Hugging Face pipeline API to load the model and generate code.

Installation

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

pip install -U transformers accelerate

Usage Example

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

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

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

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=16384)
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)

✨ Features

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 - Reasoning model, which has the following features:

Type: Causal language models
Training Stage: Pretraining & Post - training
Data Source: Public datasets
Context Length: 65,536

📦 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.

📚 Documentation

Evaluation

Seed - Coder - 8B - Reasoning strikes impressive performance on competitive programming, demonstrating that smaller LLMs can also be competent on complex reasoning tasks. Our model surpasses QwQ - 32B and DeepSeek - R1 on IOI'2024, and achieves an ELO rating comparable to o1 - mini on Codeforces contests.

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.

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