Instructions to use ethicalabs/FlowerTune-Qwen2.5-Coder-0.5B-Instruct-PEFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ethicalabs/FlowerTune-Qwen2.5-Coder-0.5B-Instruct-PEFT with PEFT:
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-PEFT") - Notebooks
- Google Colab
- Kaggle
Model Card for FlowerTune-Qwen2.5-Coder-0.5B-Instruct-PEFT
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 (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.
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:
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")
Communication Budget
8766.51 MB Megabytes
Virtual Machine Details
For this experiment, I utilized CUDO Compute as the GPU compute provider.
| Component | Specification |
|---|---|
| GPU | 1 × RTX A4000 16 GB |
| vCPUs | 4 |
| CPU | AMD EPYC (Milan) |
| Memory | 16 GB |
Cost Breakdown
Compute Costs
| Component | Details | Cost/hr |
|---|---|---|
| vCPUs | 4 cores | $0.0088/hr |
| Memory | 16 GB | $0.056/hr |
| GPU | 1 × RTX A4000 | $0.25/hr |
Storage Costs
| Component | Details | Cost/hr |
|---|---|---|
| Boot Disk Size | 70 GB | $0.0077/hr |
Network Costs
| Component | Details | Cost/hr |
|---|---|---|
| Public IPv4 Address | N/A | $0.005/hr |
Total Cost
| Total Cost/hr |
|---|
| $0.3275/hr |
Simulation Details
| Parameter | Value |
|---|---|
| Runtime | 1924.52 seconds (00:32:04) |
| Simulation Cost | $0.18 |
Framework versions
- PEFT 0.14.0
- Flower 1.13.1
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Model tree for ethicalabs/FlowerTune-Qwen2.5-Coder-0.5B-Instruct-PEFT
Base model
Qwen/Qwen2.5-0.5B