Text Generation
PEFT
Safetensors
English
rail
track-geometry
lora
defect-detection
49-cfr-213
conversational
Instructions to use BenGyi/rtg-llama-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use BenGyi/rtg-llama-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Meta-Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "BenGyi/rtg-llama-lora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad1f5ceb295a2871d277c817bae0cb0ad5ca8049fbb9b8c03ed45265b47d8f77
- Size of remote file:
- 5.78 kB
- SHA256:
- 26765840e6c9ef055bac21155fbba6cf7a28fecce09232b12c6a2c0a1b9d0c2b
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