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llm-finetuned elsa entity-level sentiment-analysis
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Instructions to use rajiv-data-chef/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rajiv-data-chef/outputs with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("abacusai/Llama-3-Smaug-8B") model = PeftModel.from_pretrained(base_model, "rajiv-data-chef/outputs") - Notebooks
- Google Colab
- Kaggle
outputs
This model is a fine-tuned version of abacusai/Llama-3-Smaug-8B on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 20
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.11.1
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for rajiv-data-chef/outputs
Base model
abacusai/Llama-3-Smaug-8B