File size: 1,879 Bytes
4d376f6 1c762c5 4d376f6 1c762c5 4d376f6 1c762c5 4d376f6 1c762c5 67c25b2 4d376f6 1c762c5 4d376f6 1c762c5 4d376f6 1c762c5 4d376f6 1c762c5 4d376f6 1c762c5 4d376f6 1c762c5 4d376f6 1c762c5 4d376f6 1c762c5 4d376f6 1c762c5 4d376f6 67c25b2 1c762c5 4d376f6 1c762c5 | 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 | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: hishab/titulm-1b-bn-v1
datasets:
- crosssum
model-index:
- name: llm_bn_sum
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. -->
# llm_bn_sum
This model is a fine-tuned version of [hishab/titulm-1b-bn-v1](https://huggingface.co/hishab/titulm-1b-bn-v1) on the crosssum dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0 | 1.0 | 2023 | nan |
| 0.0 | 2.0 | 4046 | nan |
| 0.0 | 3.0 | 6069 | nan |
| 0.0 | 4.0 | 8092 | nan |
| 0.0 | 5.0 | 10115 | nan |
| 0.0 | 6.0 | 12138 | nan |
| 0.0 | 7.0 | 14161 | nan |
| 0.0 | 8.0 | 16184 | nan |
| 0.0 | 9.0 | 18207 | nan |
| 0.0 | 10.0 | 20230 | nan |
### Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.1.0
- Tokenizers 0.15.2 |