DeepSeek Coder V2: A major update to DeepSeek Coder, a coding model developed by DeepSeek AI, a leading AI research entity from China.
Improvements: Introduced with 6 trillion additional tokens and advanced pre-training techniques, focused on coding capabilities and overall LLM performance.
Comparison: Outperforms GPT-4 Turbo, Cloud 3, and even Google's Gemini 1 along with Codestrol by Mistol AI.
Core Features and Improvements
Model Architecture
Mixture of Experts: A model architecture employing 236 billion parameters with 21 billion active at any given time.
Context Length: Expanded from 16,000 to 128,000 tokens, providing a wider context window.
Language Support: Supports over 338 programming languages.
Training Data: Enhanced with 6 trillion additional tokens, with 60% code, 10% math, and 30% natural language.
Performance Benchmarks
Benchmarks: Exceeds GPT-4 Turbo and other models in coding and math tasks.
Notable Benchmarks: HumanEval, GSM 8K, and MBPP.
Codal: Surprisingly lower performance despite being a strong model.
Cloud 3: Solid state-of-the-art measure.
Training Methodology and Techniques
Data Sources and Pre-Training
Sources: Additional 6 trillion tokens from GitHub and Common Crawl, including raw source code, math corpus, and natural language.
Process: Initiated with extensive pre-training followed by supervised fine-tuning and reinforcement learning.
Reinforcement Learning: Uses Group Relative Policy Optimization (GRPO) instead of the more common DPO.
Fine-Tuning and Optimization
Test Case Feedback: Utilizes test cases for coding task feedback for optimization.
Learned Reward Model: Enhances response correctness and human preference.
Supervised Fine Tuning: On specialized code and general instruction data.
Practical Applications and Usage
Coding Applications
Programming Languages: Covers traditional and exotic languages including AMD GPU, Elixir, VHDL, and Nginx config files.
Common Tasks: Simplifies large codebases, suggests optimizations, and improves code readability.
Available Models: Offered on Hugging Face and DeepSeek AI GitHub in both instruct and chat modes.
Example Use Cases
Python Functions: E.g., writing a Mandelbrot set estimator function, complete with comments and test cases.
ASIC Design: Capable of creating an ASIC for Bitcoin mining using VHDL, showing advanced understanding.
General Inquiries: Handles non-coding tasks well, e.g., generating thematic sentences.
Evaluation and Insights
Strengths: High performance on complex coding tasks, extensive support for programming languages, and open-source availability.
Weaknesses: N/A from provided information, but some skepticism on the claimed full language support.
Conclusion
Recommendation: Positioned as a leading choice for rigorous coding applications and available for integration directly into coding environments.
Community Feedback: Encourages users to adopt and evaluate the model, especially for local non-closed source applications.
Call to Action
Usage: Experiment with DeepSeek Coder V2 on Hugging Face or DeepSeek AI GitHub.
Feedback: Participate in community discussions to share experiences and insights about the model.