outputs
This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct and is intended for text classification tasks. It has been trained to classify text based on the provided labels in the training dataset.
Model description
More information needed
Intended uses & limitations
This model is intended for text classification tasks such as sentiment analysis, spam detection, or other binary/multiclass classification problems.
Limitations:
- The model might not perform well on tasks it has not been explicitly trained for.
- The performance may vary depending on the domain and the quality of the input data.
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 3407
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
Training results
Framework versions
- PEFT 0.12.0
- Transformers 4.43.3
- Pytorch 2.4.0+cu124
- Datasets 2.20.0
- Tokenizers 0.19.1
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Inference API (serverless) does not yet support peft models for this pipeline type.
Model tree for WinstonShum/lora_model_llama_3.1_8b_instruct_finetuned
Base model
meta-llama/Llama-3.1-8B
Finetuned
meta-llama/Llama-3.1-8B-Instruct