README.md

metadata

license: apache-2.0
base_model:
  - ByteDance-Seed/Seed-Coder-8B-Base

Seed-Coder-8B-Reasoning

Introduction

Seed-Coder-8B-Reasoning is an 8-billion-parameter model further optimized for code reasoning, problem-solving, and algorithmic thinking tasks.
Built upon the strong base of Seed-Coder, it undergoes additional training in sandbox environments to significantly enhance its ability to tackle complex coding problems and competitions. It features:

• Trained on a massively curated corpus, filtered using an LLM-based method to ensure high-quality real-world code, text-code alignment, and synthetic datasets.
• Sandbox fine-tuning to specifically strengthen multi-step reasoning, algorithm design, and competitive programming capabilities.
• Maintains long-context handling up to 32K tokens, enabling it to reason over extended problem descriptions and large input-output examples.

Model Downloads

Model Name	Type	Length	Download
Seed-Coder-8B-Base	base	32k	🤗 Hugging Face
Seed-Coder-8B-Instruct	instruct	32k	🤗 Hugging Face
👉Seed-Coder-8B-Reasoning	reasoning	32k	🤗 Hugging Face

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 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)

Evaluation

Seed-Coder-8B-Reasoning has been evaluated extensively on reasoning-intensive code benchmarks, showing:

• Significant improvements on competitive programming datasets and coding challenges.
• Enhanced ability to break down complex problems, design correct algorithms, and produce efficient implementations.
• Strong generalization to unseen problems across multiple domains (math, strings, arrays, graphs, DP, etc.).

For detailed results, please check our paper.