mrs83/FlowerTune-Qwen2.5-7B-Instruct-Medical-PEFT

Model Card for FlowerTune-Qwen2.5-7B-Instruct-Medical-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.

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 Medical Leaderboard.

Model Details

Please check the following GitHub project for model details and evaluation results:

https://github.com/mrs83/FlowerTune-Qwen2.5-7B-Instruct-Medical

Training procedure

The following bitsandbytes quantization config was used during training:

• —quant_method: bitsandbytes
• —loadin_8bit: False
• —loadin_4bit: True
• —llmint8threshold: 6.0
• —llmint8skip_modules: None
• —llmint8enablefp32cpu_offload: False
• —llmint8hasfp16weight: False
• —bnb4bitquant_type: fp4
• —bnb4bitusedoublequant: False
• —bnb4bitcompute_dtype: float32
• —bnb4bitquant_storage: uint8
• —loadin4bit: True
• —loadin8bit: False

Framework versions

• —PEFT 0.6.2
• —Flower 1.12.0