Refact Code LLM: 1.6B LLM for code that reaches 32% HumanEval
Analysis
This article highlights a 1.6 billion parameter language model (LLM) specifically designed for code generation, achieving a 32% score on the HumanEval benchmark. This suggests progress in smaller-scale, specialized LLMs for coding tasks. The focus on HumanEval indicates an attempt to quantify performance against human-level coding ability.
Key Takeaways
- •A 1.6B parameter LLM for code generation is presented.
- •The model achieves 32% on the HumanEval benchmark.
- •This suggests progress in smaller, specialized LLMs for coding.
Reference
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