Model-text-generation
This model is a fine-tuned version of bigscience/bloomz-560m on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.6440
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: 1.41e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.6532 | 1.0 | 984 | 3.6657 |
| 3.6527 | 2.0 | 1968 | 3.6518 |
| 3.6301 | 3.0 | 2953 | 3.6462 |
| 3.6279 | 4.0 | 3937 | 3.6442 |
| 3.6385 | 5.0 | 4920 | 3.6440 |
Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for tr-aravindan/Model-text-generation
Base model
bigscience/bloomz-560m
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m") model = PeftModel.from_pretrained(base_model, "tr-aravindan/Model-text-generation")