File size: 3,331 Bytes
b2f9a13 e3c4c99 b2f9a13 e3c4c99 b2f9a13 e3c4c99 b2f9a13 |
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 93 94 95 96 97 98 99 100 101 102 103 104 |
---
base_model: ondevicellm/tinyllama_mole_v1
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: tinyllama_mole_sft_router05_lr1e-4_ep3
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. -->
# tinyllama_mole_sft_router05_lr1e-4_ep3
This model is a fine-tuned version of [ondevicellm/tinyllama_mole_v1](https://huggingface.co/ondevicellm/tinyllama_mole_v1) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1035
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 120
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2617 | 0.09 | 100 | 2.2410 |
| 2.2246 | 0.18 | 200 | 2.2165 |
| 2.1994 | 0.26 | 300 | 2.1994 |
| 2.1767 | 0.35 | 400 | 2.1869 |
| 2.1532 | 0.44 | 500 | 2.1792 |
| 2.171 | 0.53 | 600 | 2.1717 |
| 2.1588 | 0.61 | 700 | 2.1645 |
| 2.145 | 0.7 | 800 | 2.1567 |
| 2.1366 | 0.79 | 900 | 2.1507 |
| 2.1219 | 0.88 | 1000 | 2.1450 |
| 2.1415 | 0.96 | 1100 | 2.1387 |
| 1.9765 | 1.05 | 1200 | 2.1446 |
| 1.9837 | 1.14 | 1300 | 2.1430 |
| 1.9952 | 1.23 | 1400 | 2.1388 |
| 1.9868 | 1.31 | 1500 | 2.1351 |
| 1.9864 | 1.4 | 1600 | 2.1316 |
| 1.987 | 1.49 | 1700 | 2.1263 |
| 1.9678 | 1.58 | 1800 | 2.1230 |
| 1.9827 | 1.66 | 1900 | 2.1164 |
| 1.9846 | 1.75 | 2000 | 2.1134 |
| 1.9694 | 1.84 | 2100 | 2.1068 |
| 1.9429 | 1.93 | 2200 | 2.1035 |
| 1.8079 | 2.01 | 2300 | 2.1369 |
| 1.8132 | 2.1 | 2400 | 2.1375 |
| 1.8043 | 2.19 | 2500 | 2.1360 |
| 1.7927 | 2.28 | 2600 | 2.1334 |
| 1.7935 | 2.37 | 2700 | 2.1335 |
| 1.7982 | 2.45 | 2800 | 2.1321 |
| 1.8029 | 2.54 | 2900 | 2.1311 |
| 1.7919 | 2.63 | 3000 | 2.1298 |
| 1.7953 | 2.72 | 3100 | 2.1287 |
| 1.798 | 2.8 | 3200 | 2.1280 |
| 1.7947 | 2.89 | 3300 | 2.1282 |
| 1.8015 | 2.98 | 3400 | 2.1283 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|