llama3-8b-summarize-gpt4o-128k
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the llama-duo/synth_summarize_dataset_dedup dataset. It achieves the following results on the evaluation set:
- Loss: 2.2606
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: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.8176 | 0.9954 | 109 | 2.1150 |
| 0.7464 | 2.0 | 219 | 2.1313 |
| 0.7128 | 2.9954 | 328 | 2.1444 |
| 0.6924 | 4.0 | 438 | 2.1631 |
| 0.6777 | 4.9954 | 547 | 2.1823 |
| 0.6526 | 6.0 | 657 | 2.2078 |
| 0.6326 | 6.9954 | 766 | 2.2296 |
| 0.6311 | 8.0 | 876 | 2.2485 |
| 0.6233 | 8.9954 | 985 | 2.2587 |
| 0.6194 | 9.9543 | 1090 | 2.2606 |
Framework versions
- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.2.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for llama-duo/llama3-8b-summarize-gpt4o-128k
Base model
meta-llama/Meta-Llama-3-8B