Text Generation
PEFT
Safetensors
English
lora
qwen3
devops
kubernetes
docker
sre
infrastructure
ci-cd
automation
troubleshooting
github-actions
production-ready
Eval Results (legacy)
Instructions to use AMaslovskyi/qwen-devops-foundation-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AMaslovskyi/qwen-devops-foundation-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B") model = PeftModel.from_pretrained(base_model, "AMaslovskyi/qwen-devops-foundation-lora") - Notebooks
- Google Colab
- Kaggle
Commit History
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Update README with comprehensive system requirements and performance expectations
Andrii Maslovskyi- Added detailed memory and storage requirements for optimal performance. - Included hardware compatibility information across various platforms. - Documented device categories and their respective performance metrics. - Specified software dependencies for core and optional packages to aid setup.5d7be8b -
Enhance README with detailed model evaluation and deployment guidance
Andrii Maslovskyi- Added performance highlights, including accuracy and speed metrics. - Included comprehensive evaluation results across various DevOps categories. - Documented local and cloud deployment options with example code snippets. - Expanded sections on strengths, use cases, and areas for enhancement to improve clarity and usability.95cabdb -
Updated README
Verified signatureb7c22a1 -
Add Qwen DevOps Foundation LoRA adapter - 4xL40S trained
Verified signature- Fine-tuned Qwen3-8B on DevOps datasets using LoRA - Trained on 4x NVIDIA L40S GPUs for ~3 hours - Supports Docker, Kubernetes, CI/CD, and SRE tasks - 174MB LoRA adapter + tokenizer files - Ready for inference with base Qwen/Qwen3-8B modela6d0d22 -
initial commit
Verified signature30c8503