File size: 2,657 Bytes
73b1528 b59e690 73b1528 b59e690 73b1528 b59e690 73b1528 |
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 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
---
library_name: transformers
license: llama3.2
base_model: tanliboy/llama-3.2-3b
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- tanliboy/OpenHermes-2.5-reformat
model-index:
- name: llama-3.2-3b-sft-2
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. -->
# llama-3.2-3b-sft-2
This model is a fine-tuned version of [tanliboy/llama-3.2-3b](https://huggingface.co/tanliboy/llama-3.2-3b) on the tanliboy/OpenHermes-2.5-reformat dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6744
## 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.7792 | 0.0673 | 500 | 0.7726 |
| 0.7496 | 0.1345 | 1000 | 0.7444 |
| 0.7243 | 0.2018 | 1500 | 0.7296 |
| 0.7178 | 0.2691 | 2000 | 0.7197 |
| 0.7077 | 0.3363 | 2500 | 0.7127 |
| 0.6992 | 0.4036 | 3000 | 0.7066 |
| 0.6992 | 0.4708 | 3500 | 0.7012 |
| 0.6945 | 0.5381 | 4000 | 0.6965 |
| 0.6879 | 0.6054 | 4500 | 0.6920 |
| 0.6901 | 0.6726 | 5000 | 0.6879 |
| 0.6759 | 0.7399 | 5500 | 0.6844 |
| 0.6752 | 0.8072 | 6000 | 0.6812 |
| 0.6826 | 0.8744 | 6500 | 0.6783 |
| 0.6804 | 0.9417 | 7000 | 0.6758 |
| 0.6131 | 1.0089 | 7500 | 0.6764 |
| 0.6012 | 1.0762 | 8000 | 0.6758 |
| 0.6136 | 1.1435 | 8500 | 0.6751 |
| 0.6127 | 1.2107 | 9000 | 0.6747 |
| 0.6076 | 1.2780 | 9500 | 0.6745 |
| 0.6033 | 1.3453 | 10000 | 0.6744 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|