| --- |
| license: mit |
| datasets: |
| - flwrlabs/code-alpaca-20k |
| language: |
| - en |
| metrics: |
| - accuracy |
| base_model: |
| - microsoft/Phi-4-mini-instruct |
| pipeline_tag: text-generation |
| library_name: peft |
| tags: |
| - text-generation-inference |
| - code |
| --- |
| |
| > [!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**: 59.76 % |
| - **MBPP**: 46.20 % |
| - **MultiPL-E (C++)**: 37.27 % |
| - **MultiPL-E (JS)**: 52.79 % |
| - **Average**: 49.00 % |
|
|
| ## Model Details |
|
|
| This PEFT adapter has been trained by using [Flower](https://flower.ai/), a friendly federated AI framework. |
|
|
| The adapter and benchmark results have been submitted to the [FlowerTune LLM Code Leaderboard](https://flower.ai/benchmarks/llm-leaderboard/code/). |
|
|
| Please check the following GitHub project for details on how to reproduce training and evaluation steps: |
|
|
| [FlowerTune-LLM-Labs](https://github.com/ethicalabs-ai/FlowerTune-LLM-Labs/blob/main/workspace/models/README.md) |
