Instructions to use VishalCh/trained-llama2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VishalCh/trained-llama2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("VishalCh/trained-llama2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
trained-llama2
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf 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: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 30
Training results
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
- Downloads last month
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Model tree for VishalCh/trained-llama2
Base model
meta-llama/Llama-2-7b-hf
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("VishalCh/trained-llama2", dtype="auto")