Introduction

In the fast-paced world of artificial intelligence, large language models (LLMs) have become indispensable tools across various domains. Code generation models, in particular, have emerged as invaluable assets for developers looking to enhance productivity and efficiency. ByteDance’s Seed-Coder model family stands out as a significant contribution to this field. As an open-source code LLM family with 8 billion parameters, Seed-Coder is designed to minimize human effort in data construction while maximizing code generation capabilities.

Overview of Seed-Coder

Model Composition

Seed-Coder comprises three main models: Base, Instruct, and Reasoning. Each model is built on an 8B parameter scale, offering a robust foundation for code generation tasks.

  • Base Model: This is the foundational model of the family, trained on large-scale code data to learn the structure, logic, and patterns of code.
  • Instruct Model: Fine-tuned on the Base Model, the Instruct Model is optimized to understand and follow user instructions accurately, generating code that meets user expectations.
  • Reasoning Model: Focused on enhancing multi-step reasoning capabilities, this model employs reinforcement learning (RL) to tackle complex code tasks.

Self-Reinforcing Data Pipeline

What sets Seed-Coder apart is its innovative approach to data curation. Unlike traditional models that rely on hand-crafted rules for data filtering, Seed-Coder leverages LLMs themselves to evaluate and filter code data. This method not only reduces manual intervention but also improves the precision of data quality assessment.

Data Sources and Preprocessing

The training data for Seed-Coder is sourced from GitHub code data, code commit data, and code-related web data. ByteDance’s team implemented a series of preprocessing steps, including deduplication, syntax checking, and model-based scoring, to ensure the quality and diversity of the data.

Performance Highlights

Base Model Performance

The Seed-Coder-8B-Base model has demonstrated exceptional performance in multiple code benchmarks. For instance, it achieved a remarkable pass rate of 77.4% in the HumanEval test and 68.3% in the MBPP test.

Instruct Model Performance

The Seed-Coder-8B-Instruct model excels in instruction-following and code generation tasks. It achieved an impressive pass rate of 84.8% in the HumanEval test and 36.2% in the MHPP test.

Reasoning Model Performance

The Seed-Coder-8B-Reasoning model stands out for its advanced reasoning capabilities. In the LiveCodeBench test, it achieved an overall pass rate of 53.6%, with significant improvements of 22.8% for medium-difficulty problems and 14.0% for hard problems.

Getting Started with Seed-Coder

Model Deployment Examples

For developers eager to integrate Seed-Coder into their workflow, here are two deployment examples:

Using the transformers Library

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) 

Using the vLLM Library

from transformers import AutoTokenizer from vllm import LLM, SamplingParams  tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Instruct")  sampling_params = SamplingParams(temperature=0.6, top_p=0.8, repetition_penalty=1.05, max_tokens=512)  llm = LLM(model="ByteDance-Seed/Seed-Coder-8B-Instruct")  prompt = "#write a quick sort algorithm."  outputs = llm.generate([prompt], sampling_params)  for output in outputs:     prompt = output.prompt     generated_text = output.outputs[0].text     print(f"Prompt: {prompt!r}\n\nGenerated content: {generated_text!r}") 

Moreover, vLLM supports multi-GPU distributed inference, which can significantly boost throughput when dealing with long-context inputs, such as 32K tokens. Here’s an example of how to utilize this feature:

llm = LLM(model="ByteDance-Seed/Seed-Coder-8B-Instruct", tensor_parallel_size=8) 

Data Processing and Training Strategies

Data Screening and Preprocessing

The training data for Seed-Coder undergoes rigorous screening and preprocessing. This includes deduplication, syntax checking, and model-based scoring to ensure high-quality data for training.

Training Strategy

The training process of Seed-Coder is divided into several stages:

  1. Base Model Training: Initiated with code-related web data and mathematical web data to build the model’s foundational understanding of code.
  2. Continuous Training: Enhanced by incorporating high-quality and long-context datasets to boost the model’s performance and generalization ability.
  3. Learning Rate Adjustment: The learning rate starts at 3e−4 and is gradually reduced to 3e−5 during the continuous training phase.

Future Outlook

Seed-Coder represents a significant step forward in the field of open-source code LLMs. However, there is still room for improvement, especially in general natural language understanding and handling a broader range of tasks. ByteDance’s Seed team plans to continue refining Seed-Coder, with a focus on enhancing its code-related capabilities and expanding its application scope.

Currently, Seed-Coder’s pre-training data volume stands at 6 trillion tokens, which is relatively small compared to models pre-trained on 36 trillion tokens. This limitation suggests potential areas for future development, where the model could expand its knowledge and capabilities in other domains while maintaining its strength in code intelligence.

About ByteDance’s Seed Team

Established in 2023, ByteDance’s Seed team is dedicated to exploring new approaches to general intelligence and pushing the boundaries of AI. With a long-term vision and commitment to foundational research, the team aims to become a world-class AI research group that drives real technological progress and delivers societal benefits. The team has labs across China, Singapore, and the U.S., and has already released industry-leading general-purpose large models and advanced multimodal capabilities, powering over 50 real-world applications.

Conclusion

Seed-Coder offers a new and powerful tool for the code generation community. Its innovative self-curated data approach, superior performance, and open-source nature make it a valuable asset for developers and researchers alike. As the ByteDance Seed team continues to advance this model family, we can expect even more impressive achievements in the field of code intelligence.