Instructions to use Cem13/lora_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cem13/lora_model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Cem13/lora_model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use Cem13/lora_model 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 Cem13/lora_model 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 Cem13/lora_model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Cem13/lora_model to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Cem13/lora_model", max_seq_length=2048, )
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
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- adapter_model.safetensors +1 -1
adapter_config.json
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"base_model_name_or_path": "unsloth/qwen2.5-14b-unsloth-bnb-4bit",
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"bias": "none",
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"rank_pattern": {},
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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"base_model_name_or_path": "unsloth/qwen2.5-14b-unsloth-bnb-4bit",
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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