Text Classification
PEFT
Safetensors
Transformers
LoRA
QLoRA
multi-label
decoder-only
trl
bitsandbytes
Instructions to use Amirhossein75/LLM-Decoder-Tuning-Text-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Amirhossein75/LLM-Decoder-Tuning-Text-Classification with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("meta-llama/Llama-3.2-1B") model = PeftModel.from_pretrained(base_model, "Amirhossein75/LLM-Decoder-Tuning-Text-Classification") - Transformers
How to use Amirhossein75/LLM-Decoder-Tuning-Text-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Amirhossein75/LLM-Decoder-Tuning-Text-Classification")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Amirhossein75/LLM-Decoder-Tuning-Text-Classification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbcd7733ae6d0bb2d66865266f3377f9f2f758ee0cdcddc8d381346f4ec99324
- Size of remote file:
- 14.6 kB
- SHA256:
- 7066f5af4447095c9c42c530fd8e76bc4b1cfbc6945fb520a6c4b7b5049a794c
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