StarCoder (BigCode)

Basic Information

• Organization: BigCode (an open science collaboration between Hugging Face and ServiceNow)
• Country/Region: International collaboration (primarily France/USA)
• Official Website: https://www.bigcode-project.org
• Hugging Face: https://huggingface.co/bigcode
• GitHub: https://github.com/bigcode-project/starcoder
• Type: Open-source code generation model
• License: BigCode OpenRAIL-M v1

Product Description

StarCoder is an open-source large language model created by the BigCode community, focusing on code generation. BigCode is an open science collaboration dedicated to responsibly training large language models for coding applications. StarCoder is trained on licensed GitHub code (The Stack dataset) and is specifically designed for code generation, completion, and understanding tasks.

Model Versions

StarCoder (First Generation)

• 15.5B parameters
• Trained on The Stack dataset (licensed GitHub code)
• Multi Query Attention
• 8192 Token context window
• Fill-in-the-Middle objective training
• 1 trillion Token training data

StarCoder 2 (Second Generation)

• Three sizes: 3B, 7B, 15B
• Trained on The Stack v2 (600+ programming languages)
• 3.3 to 4.3 trillion Token training data
• GQA attention mechanism
• 16K context window (sliding window attention)
• Enhanced performance and efficiency

Core Features

• Code generation and completion
• Fill-in-the-Middle (code infilling)
• Support for 600+ programming languages (StarCoder 2)
• Multiple parameter sizes for different scenarios
• Trained on license-compliant data
• Open weights and training process

Business Model

Completely open-source and free. The BigCode OpenRAIL-M license allows commercial use with certain responsibility limitations. The model is available via Hugging Face.

Target Users

• AI researchers
• Enterprises needing open-source code models
• Developers of code assistance tools
• Users concerned with training data compliance

Competitive Advantages

• License-compliant training data (only licensed code used)
• Open science methodology (transparent training process)
• Continuous maintenance by the BigCode community
• Easy access and deployment via Hugging Face
• Multilingual support (600+ programming languages)

Market Performance

• A key choice among open-source code models
• Widely cited in academia and industry
• The Stack dataset has become a standard reference for code training data
• Faces competition from DeepSeek Coder, CodeLlama, and others

Relationship with OpenClaw

StarCoder can be used as a local LLM option for OpenClaw. Its license-compliant training data makes it suitable for users sensitive to data provenance. BigCode's open science philosophy aligns with OpenClaw's open-source ethos.