DeepCoder-14B NEW Opensource Coding Model Beats 03-Mini! (Tested)
AI Summary
Deepcoder Overview
- Introduced by Together AI as a new open-source AI coding model
- 14 billion parameters, matching performance of OpenAI’s 03 Mini
- Trained on 24K verified coding problems using 32 H00 GPUs
- Achieved a 60.6% pass rate on live benchmarks, 95.3 percentile on Codeforces
Training Details
- Curated high-quality dataset of 24K coding problems
- Isolated sandbox environment for stable training
- Employed a strict reward system for successful code
- Gradually increased model context length up to 64K tokens
- Optimized training process to reduce time by half
Performance Comparison
- Benchmarked against models like 03 Mini, O1, Deep Seek R1, Llama 4 Behemoth
- Performs competitively despite smaller parameter size
Practical Usage
- Accessible via HuggingFace, LM Studio, chat-based UI systems
- Can experiment using GHF for free with $10 credits
- Simple demo: Created a functional CRM dashboard app
Task Performance
- Capable of generating SVG illustrations
- Successfully debugged faulty code with minor issues
- Demonstrated good performance in front-end development
Conclusion
- Recommended for those without resources for larger models
- Open-source nature allows full access to weights and training data