DeepCoder: Distilling Multiple Large-Scale Models for Efficient and High-Quality Code Generation
Skytells AI Research, Inc.
Abstract
DeepCoder is a novel AI model designed specifically for efficient, high-quality code generation. Created at Skytells AI Research, Inc., DeepCoder integrates knowledge distilled from three state-of-the-art large language models (DeepSeek R1, LLaMA 70B, and Qwen 72B). By merging these distinct teacher models into a single, resource-friendly system, DeepCoder achieves fast inference speeds without sacrificing code-generation quality. During finetuning, DeepCoder was trained on over 130k curated code samples from multiple programming languages. Although the model remains proprietary, it powers the SaaS platform DeepCoder.co, where developers can leverage its features to scaffold, develop, and deploy full-stack applications.
Introduction
Large Language Models (LLMs) have shown considerable success in various natural language processing tasks, including code generation, completion, and software design. However, deploying these models in real-world environments is often challenging due to their significant size and computational demands. Distillation and model compression techniques can help alleviate these constraints by transferring knowledge from one or more large-scale teacher models to a smaller, student model.
DeepCoder combines expertise from three specialized models:
- DeepSeek R1: Recognized for its enhanced contextual understanding and fine-grained code completions.
- LLaMA 70B: A generalized language model with robust abilities to handle diverse coding tasks.
- Qwen 72B: Specialized in long-context reasoning for large-scale software development.
Key Contributions
- Multi-teacher distillation approach merging multiple large-scale language models into a single, resource-efficient student model
- Training pipeline leveraging 130k curated code samples across multiple programming languages
- Full-stack development support through the DeepCoder.co SaaS platform
Architecture & Training
DeepCoder's base structure is a transformer-based decoder architecture modified to optimize parameter usage. The training process involves three main stages:
- Logit Alignment: Student model learns to match teacher ensemble's probability distributions
- Layer Feature Matching: Intermediate hidden states guide nuanced internal representations
- Task-Specific Fine-tuning: Post-distillation optimization for improved quality
Training Details
| Parameter | Value |
|---|---|
| Batch size | 256 sequences |
| Sequence length | 1024 tokens |
| Initial learning rate | 1 × 10⁻⁴ |
| Distillation epochs | 15 |
| Fine-tuning epochs | 3-5 |
DeepCoder.co Platform
The DeepCoder model powers our SaaS platform, offering:
- AI Assistant Integration: Chat interface for code generation, debugging, and reviews
- Full-Stack Code Generation: Automated project scaffolding for both front-end and back-end development
- In-Browser IDE: Real-time previews, integrated terminal, and environment configuration
- Git Integration: Seamless version control system integration
Future Work
- Additional Programming Languages: Expanding coverage to specialized or low-level languages
- Multimodal Integration: Incorporating design artifacts for holistic code generation
- Contextual Retrieval: Leveraging public and private code repositories
- Reinforcement Learning from Human Feedback (RLHF): Iterative improvements based on developer interactions
Licensing and Contact
While DeepCoder itself is not open-sourced, Skytells AI Research, Inc. offers various tools and utilities via the skytells-research GitHub organization.
For research collaborations, API access, or enterprise inquiries, please contact us.