---
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