flwrlabs/code-alpaca-20k
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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")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 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/
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")
8766.51 MB Megabytes
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 |
| Component | Details | Cost/hr |
|---|---|---|
| vCPUs | 4 cores | $0.0088/hr |
| Memory | 16 GB | $0.056/hr |
| GPU | 1 × RTX A4000 | $0.25/hr |
| Component | Details | Cost/hr |
|---|---|---|
| Boot Disk Size | 70 GB | $0.0077/hr |
| Component | Details | Cost/hr |
|---|---|---|
| Public IPv4 Address | N/A | $0.005/hr |
| Total Cost/hr |
|---|
| $0.3275/hr |
| Parameter | Value |
|---|---|
| Runtime | 1924.52 seconds (00:32:04) |
| Simulation Cost | $0.18 |
Base model
Qwen/Qwen2.5-0.5B