File size: 2,160 Bytes
e40c236 ef7f6ca e40c236 ef7f6ca e40c236 ef7f6ca e40c236 ef7f6ca e40c236 ef7f6ca e40c236 ef7f6ca e40c236 ef7f6ca e40c236 ef7f6ca e40c236 ef7f6ca e40c236 02095b3 e40c236 ef7f6ca e40c236 02095b3 ef7f6ca e40c236 ef7f6ca e40c236 ef7f6ca e40c236 02095b3 e40c236 02095b3 e40c236 ef7f6ca e40c236 ef7f6ca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
---
license: bigscience-bloom-rail-1.0
base_model: bigscience/bloom-1b7
tags:
- generated_from_trainer
model-index:
- name: Bloom-1b7-ropes-IT-baseline
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bloom-1b7-ropes-IT-baseline
This model is a fine-tuned version of [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
Instruction Tuned on the ropes task here: https://huggingface.co/datasets/adambjorn/UnrelatedForgettingOverhead/viewer/ropes
## Training procedure
Given a set of prompts:
``` python
prompts = [
"Given the following background and situation, answer the question: ",
"Based on the background information and the current situation, what is the answer to the question? ",
"Considering the background and the described situation, provide an answer to this question: ",
]
```
Each example is concatenated with the prompt, background, situation, question and answer:
``` python
input_text = f"{prompt}Background: {background} Situation: {situation} Question: {question} Answer: {answer_text}."
```
### 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 results: {'loss': 0.024, 'grad_norm': 1.3331243991851807, 'learning_rate': 8.000000000000001e-07, 'epoch': 10.0}
Average results: {'train_runtime': 862.219, 'train_samples_per_second': 2.32, 'train_steps_per_second': 0.58, 'train_loss': 0.4160269268453121, 'epoch': 10.0}
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|