Instructions to use vaishnavkoka/fine_tune_llama_sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaishnavkoka/fine_tune_llama_sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vaishnavkoka/fine_tune_llama_sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vaishnavkoka/fine_tune_llama_sst2") model = AutoModelForSequenceClassification.from_pretrained("vaishnavkoka/fine_tune_llama_sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
datasets:
- stanfordnlp/sst2
metrics:
- accuracy
- precision
- recall
- f1
base_model:
- meta-llama/Llama-3.2-1B
library_name: transformers
tags:
- llama
- sst2
- fine
- tuned
- gemma