Text Classification
PEFT
Safetensors
English
sentiment-analysis
financial-nlp
llama
lora
qlora
4-bit precision
bitsandbytes
Eval Results (legacy)
Instructions to use NunoMotaRicardo/llama-3.1-8b-financial-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use NunoMotaRicardo/llama-3.1-8b-financial-sentiment with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("meta-llama/Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "NunoMotaRicardo/llama-3.1-8b-financial-sentiment") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c0928276848e96805c44e67cbd81b1ae70ebf273ab0c1374bdbf2d92ca08ab0
- Size of remote file:
- 5.27 kB
- SHA256:
- 3dcfe9cb7f80cca256d11b427979266d513b9e02e5d47123e2e934069b9ff0d4
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