Instructions to use MarcoBrigo11/llama3-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MarcoBrigo11/llama3-samsum with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "MarcoBrigo11/llama3-samsum") - Transformers
How to use MarcoBrigo11/llama3-samsum with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="MarcoBrigo11/llama3-samsum")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MarcoBrigo11/llama3-samsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
llama3-samsum
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the Samsumg/samsum dataset.
Model description
It is a first version and has to be improved. The challenge is to fine-tune the model using limited resources. The fine tuning was performed downsampling the dataset, under Colab free plan restrictions.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.12.0
- Transformers 4.43.2
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
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Model tree for MarcoBrigo11/llama3-samsum
Base model
meta-llama/Meta-Llama-3-8B