Dracarys2-72B-Instruct is a 72.7 billion parameter instruction-tuned language model developed by Abacus.AI, finetuned from Qwen2.5-72B-Instruct. This model is specifically optimized for coding performance, demonstrating improved scores on the LiveCodeBench benchmark compared to its base model. With a context length of 131072 tokens, it excels in code generation and execution tasks.
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Dracarys2-72B-Instruct Overview
Dracarys2-72B-Instruct is a 72.7 billion parameter instruction-tuned model developed by Abacus.AI, building upon the Qwen2.5-72B-Instruct base model. It is part of the "Smaug" series, with a specific focus on enhancing coding performance across various base models.
Key Capabilities & Performance
This model demonstrates notable improvements in coding benchmarks compared to its foundational model:
- LiveCodeBench (LCB) Scores: Dracarys2-72B-Instruct shows higher scores across key LCB metrics:
- Code Generation: 53.80 (vs. 53.03 for Qwen2.5-72B-Instruct)
- Code Execution (COT): 89.12 (vs. 88.72)
- Test Output Prediction: 59.61 (vs. 46.28)
- Code Generation Breakdown: It particularly excels in the "Easy" and "Medium" categories of Code Generation within LCB.
- Test Output Prediction Breakdown: Significant improvements are observed across all difficulty levels (Easy, Medium, Hard) for Test Output Prediction.
Use Cases
Dracarys2-72B-Instruct is particularly well-suited for applications requiring robust code generation and understanding, especially when compared to its base model. Its enhanced performance on LiveCodeBench suggests strong capabilities for tasks such as:
- Generating Python code, as demonstrated in the provided example.
- Assisting with data science coding tasks using libraries like Pandas and NumPy.
- Scenarios where accurate code execution and test output prediction are critical.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.