Context-awareness in instruction finetuning
Collection
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llama-2-qlora-ultrachat-200k-processed-indicator-0.6
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the yihanwang617/ultrachat_200k_processed_indicator_0.6_4k dataset. It achieves the following results on the evaluation set:
- Loss: 0.8975
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: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.8864 | 0.9997 | 3247 | 0.8975 |
Framework versions
- PEFT 0.12.0
- Transformers 4.40.1
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for ucla-cmllab/llama-2-qlora-ultrachat-200k-processed-indicator-0.6
Base model
meta-llama/Llama-2-7b-hf