ethicalabs/Flwr-SmolLM2-1.7B-Instruct-Coding-PEFT

[!WARNING] This repository contains experimental models designed strictly for academic evaluation and research purposes. Critical Constraints: No Production Deployment: Experimental models must not be deployed in commercial, enterprise, or mission-critical environments under any circumstances. No Liability: Experimental models are provided "as-is" without warranties of any kind. The developers assume zero liability for downstream consequences, system integration failures, or regulatory non-compliance resulting from unauthorized deployment.

Evaluation Results (Pass@1)

• —HumanEval: 30.49 %
• —MBPP: 34.00 %
• —MultiPL-E (C++): 23.60 %
• —MultiPL-E (JS): 18.63 %
• —Average: 26.68 %

Model Details

This PEFT adapter has been trained by using Flower, a friendly federated AI framework.

The adapter and benchmark results have been submitted to the FlowerTune LLM Code Leaderboard.

Please check the following GitHub project for details on how to reproduce training and evaluation steps:

FlowerTune-LLM-Labs