Model Card for FlowerTune-Qwen2.5-Coder-0.5B-Instruct-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.
Training Loss
Evaluation Results (Accuracy)
MBPP: 25.60 %
HumanEval: 37.81 %
MultiPL-E (JS): 41.00 %
MultiPL-E (C++): 32.92 %
Average: 34.34 %
Model Details
This PEFT adapter has been trained by using Flower, a friendly federated AI framework.
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")
Communication Budget
8766.51 MB Megabytes
Virtual Machine Details
For this experiment, I utilized CUDO Compute as the GPU compute provider.
