Instructions to use DiyRex/Qwen2.5-DevOps-Adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DiyRex/Qwen2.5-DevOps-Adapter with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DiyRex/Qwen2.5-DevOps-Adapter", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use DiyRex/Qwen2.5-DevOps-Adapter with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for DiyRex/Qwen2.5-DevOps-Adapter to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for DiyRex/Qwen2.5-DevOps-Adapter to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DiyRex/Qwen2.5-DevOps-Adapter to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="DiyRex/Qwen2.5-DevOps-Adapter", max_seq_length=2048, )
File size: 255 Bytes
6942104 | 1 2 3 4 5 6 7 8 9 10 11 | {
"add_prefix_space": null,
"backend": "tokenizers",
"bos_token": null,
"eos_token": "<|im_end|>",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "<|PAD_TOKEN|>",
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}
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