File size: 1,935 Bytes
501a9da bbca558 501a9da bbca558 501a9da bbca558 501a9da bbca558 501a9da bbca558 501a9da bbca558 501a9da bbca558 501a9da bbca558 501a9da bbca558 501a9da bbca558 501a9da bbca558 501a9da bbca558 501a9da bbca558 501a9da bbca558 501a9da bbca558 501a9da bbca558 501a9da bbca558 |
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 |
---
base_model: jetmoe/jetmoe-8b
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/airoboros-3.2
- HuggingFaceH4/Code-Feedback
- HuggingFaceH4/orca-math-word-problems-200k
- HuggingFaceH4/SystemChat
- HuggingFaceH4/capybara
model-index:
- name: jetmoe-8b-sft
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. -->
# jetmoe-8b-sft
This model is a fine-tuned version of [jetmoe-8b](https://huggingface.co/jetmoe/jetmoe-8b) on the HuggingFaceH4/ultrachat_200k, the HuggingFaceH4/airoboros-3.2, the HuggingFaceH4/Code-Feedback, the HuggingFaceH4/orca-math-word-problems-200k, the HuggingFaceH4/SystemChat and the HuggingFaceH4/capybara datasets.
It achieves the following results on the evaluation set:
- Loss: 0.9952
## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- 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_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2458 | 1.0 | 2049 | 0.9776 |
| 1.1966 | 2.0 | 4099 | 0.9756 |
| 1.1073 | 3.0 | 6147 | 0.9952 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
|