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
Download tokenizer.json from AMaslovskyi/qwen-devops-foundation-lora: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/AMaslovskyi/qwen-devops-foundation-lora/resolve/main/tokenizer.json
- Command line
-
hf download hf://AMaslovskyi/qwen-devops-foundation-lora/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/AMaslovskyi/qwen-devops-foundation-lora/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- cd7209d48405d98ecfe3b74ea44931d5383deb0c86cdf6a0c33e3d3f31752cb2
- Size of remote file:
- 11.4 MB
- SHA256:
- 1898b39d0bc7babd03f2570363e639f71a5b7449ab810665f12a437b297904f3
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