---
license: mit
datasets:
- lucasmccabe-lmi/CodeAlpaca-20k
language:
- en
metrics:
- accuracy
base_model:
- Qwen/Qwen2.5-Coder-0.5B-Instruct
pipeline_tag: text-generation
library_name: peft
tags:
- text-generation-inference
- code
---

# Model Card for ethicalabs/FlowerTune-Qwen2.5-Coder-0.5B-Instruct

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/).

## Model Details

Please check the following GitHub project for model details and evaluation results (Work in Progress!!!):

[https://github.com/ethicalabs-ai/FlowerTune-Qwen2.5-Coder-0.5B-Instruct/](https://github.com/ethicalabs-ai/FlowerTune-Qwen2.5-Coder-0.5B-Instruct/)

## How to Get Started with the Model

Use this model as:

```
from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "ethicalabs/FlowerTune-Qwen2.5-Coder-0.5B-Instruct")
```

### Evaluation Results (Accuracy)

- **MBPP**:  21.20 %
- **HumanEval**: 36.59 %
- **MultiPL-E (JS)**: 40.38 %
- **MultiPL-E (C++)**: 33.55 %
- **Average**: 33.00 %

### Communication Budget

8766.51 MB Megabytes

### Framework versions

- PEFT 0.14.0
- Flower 1.13.0