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---
license: llama3
language:
- gsw
datasets:
- cis-lmu/Glot500
- cis-lmu/GlotCC-V1
pipeline_tag: text-generation
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
model_type: LlamaForCausalLM
tags:
- Llama-3
- instruct
- finetune
- chatml
- synthetic data
- axolotl
---
# Alpesteibock-Llama-3-8B-Alpha
**Alpesteibock-Llama-3-8B-Alpha** is an experimental QLoRA fine-tune of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B) on a dataset of more than 28 million tokens of Swiss German text from multiple sources.
## License
This model is released under the [Llama 3 Community License](https://llama.meta.com/llama3/license/).
## Usage
The model uses ChatML as an instruction template and was trained using "You are Alpesteibock, a helpful assistant who speaks Swiss German." as a system message:
```
<|im_start|>system
You are Alpesteibock, a helpful assistant who speaks Swiss German.<|im_end|>
<|im_start|>user
Hoi. Wie heissisch du?<|im_end|>
<|im_start|>assistant
Ich bi de Alpesteibock und ich freu mi uf di.<|im_end|>
```
## Dataset
The dataset used for training consists of the following sources:
| Dataset | File Size | Description | Phase |
|---------|-----------|-------------|-------|
| [Glot500 Corpus](https://huggingface.co/datasets/cis-lmu/Glot500) (gsw_Latn, Leipzig_web) | 21.7 MB | Text, usually sentences, crawled from the web | 1 |
| [Alemannic Wikipedia](https://dumps.wikimedia.org/alswiki/) (Subset) | 50.5 MB | Articles in the Alemannic Wikipedia with most of those written in Alsatian filtered out | 2 |
| [Schweizerdeutscher Mundartkorpus](https://chmk.ch/) (Copyright Free Subset) | 28.4 MB | Copyright free books written in Swiss German | 2 |
| [GlotCC-V1.0](https://huggingface.co/datasets/cis-lmu/GlotCC-V1) (gsw-Latn) | 7.5 MB | Document-level general domain monolingual dataset derived from CommonCrawl | 2 |
| Synthetic Instruction Data | 1.7 MB | Different datasets of synthetically generated Swiss German text | 2 |
## Training Details
Hardware: 1x RTX 4090
Duration: 40 hours in total (2 hours for first phase and 38 hours for second phase)
### Hyperparameters
Adapter: QLoRA
Precision: 4-bit
Optimizer: adamw_bnb_8bit
LoRA Rank: 256
LoRA Alpha: 256
Learning Rate: 1e-5
Scheduler: Cosine
Context Length: 4096
Batch Size: 1
Gradient Accumulation Steps: 1
Sample Packing: On for first phase, Off for second phase
Epochs: 2 |