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---
license: bigscience-bloom-rail-1.0
base_model: bigscience/bloom-1b7
tags:
- generated_from_trainer
model-index:
- name: Bloom-1b7-dialogsum-IT
results: []
---
# Bloom-1b7-dialogsum-IT
This model is a instruction-tuned version of [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) on a dialog summation dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Instruction Tuned on the dialog summation task here: https://huggingface.co/datasets/adambjorn/UnrelatedForgettingOverhead/viewer/dialogsum/train
## Training procedure
Given a set of prompts:
``` python
prompts = [
"Provide a concise summary for the following dialogue:",
"Summarize this conversation in a few sentences:",
"Here is a dialogue. Can you summarize it briefly?",
"Read the following dialogue and write a short summary:",
"Condense the essence of this conversation into a summary:"
]
```
Each example is concatenated with the prompt, the dialogue, and the summary as so:
``` python
concatenated_texts = [
random.choice(prompts) + " " + dialogue + "<\s>" + " Summary:" + summary
for dialogue, summary in zip(examples['dialogue'], examples['summary'])
]
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
Final epoch results: {'loss': 0.0137, 'grad_norm': 0.6599154472351074, 'learning_rate': 7.000000000000001e-07, 'epoch': 10.0}
Average results: {'train_runtime': 1142.1524, 'train_samples_per_second': 1.751, 'train_steps_per_second': 0.438, 'train_loss': 0.37129621666669843, 'epoch': 10.0}
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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