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---
library_name: transformers
license: bigcode-openrail-m
base_model: bigcode/starcoder2-15b
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- HuggingFaceH4/airoboros-3.2
- HuggingFaceH4/Code-Feedback
- HuggingFaceH4/orca-math-word-problems-200k
- HuggingFaceH4/SystemChat
- HuggingFaceH4/capybara
model-index:
- name: starchat2-15b-v0.1
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. -->
# starchat2-15b-v0.1
This model is a fine-tuned version of [bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b) on 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: 0.6601
## 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: 32
- total_train_batch_size: 128
- total_eval_batch_size: 256
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.8402 | 0.1099 | 100 | 0.8307 |
| 0.7611 | 0.2198 | 200 | 0.7793 |
| 0.7361 | 0.3297 | 300 | 0.7525 |
| 0.6854 | 0.4396 | 400 | 0.7337 |
| 0.6926 | 0.5495 | 500 | 0.7197 |
| 0.7125 | 0.6593 | 600 | 0.7097 |
| 0.6662 | 0.7692 | 700 | 0.7015 |
| 0.6517 | 0.8791 | 800 | 0.6937 |
| 0.6234 | 0.9890 | 900 | 0.6869 |
| 0.5925 | 1.0989 | 1000 | 0.6866 |
| 0.585 | 1.2088 | 1100 | 0.6832 |
| 0.5857 | 1.3187 | 1200 | 0.6798 |
| 0.5736 | 1.4286 | 1300 | 0.6746 |
| 0.5906 | 1.5385 | 1400 | 0.6723 |
| 0.569 | 1.6484 | 1500 | 0.6686 |
| 0.5756 | 1.7582 | 1600 | 0.6655 |
| 0.545 | 1.8681 | 1700 | 0.6622 |
| 0.5505 | 1.9780 | 1800 | 0.6606 |
| 0.5149 | 2.0879 | 1900 | 0.6648 |
| 0.5234 | 2.1978 | 2000 | 0.6638 |
| 0.5239 | 2.3077 | 2100 | 0.6632 |
| 0.5142 | 2.4176 | 2200 | 0.6623 |
| 0.5086 | 2.5275 | 2300 | 0.6616 |
| 0.4998 | 2.6374 | 2400 | 0.6604 |
| 0.5029 | 2.7473 | 2500 | 0.6602 |
| 0.5146 | 2.8571 | 2600 | 0.6599 |
| 0.5293 | 2.9670 | 2700 | 0.6601 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+rocm6.2
- Datasets 3.5.0
- Tokenizers 0.20.3
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