Instructions to use bincoder/lora_model-PFG-003 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bincoder/lora_model-PFG-003 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bincoder/lora_model-PFG-003", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use bincoder/lora_model-PFG-003 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 bincoder/lora_model-PFG-003 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 bincoder/lora_model-PFG-003 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bincoder/lora_model-PFG-003 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="bincoder/lora_model-PFG-003", max_seq_length=2048, )
- Xet hash:
- 633d4eff0543730e17db2f1ddad84c2e5f0e97e591aaaaad3df1b8cb9e999639
- Size of remote file:
- 168 MB
- SHA256:
- 6100b88d1fbecc3a11a5f20071f8f2beb4d7fd2340daa1e96a49d819fde95340
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