---
license: llama2
datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
language:
- tr
- en
---



# SambaLingo-Turkish-Chat

<img src="SambaLingo_Logo.png" width="340" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

<!-- Provide a quick summary of what the model is/does. -->
SambaLingo-Turkish-Chat is a bi-lingual human aligned chat model trained for Turkish and English. It is trained using direct preference optimization on top the base model [SambaLingo-Turkish-Base](https://huggingface.co/sambanovasystems/SambaLingo-Turkish-Base). The base model adapts [Llama 2](https://huggingface.co/meta-llama/Llama-2-7b-hf) to Turkish by training on 63 billion tokens from the Turkish split of the [Cultura-X](https://huggingface.co/datasets/uonlp/CulturaX) dataset.

## Model Description
<!-- Provide a longer summary of what this model is. -->

- **Developed by:** [SambaNova Systems](https://sambanova.ai/)
- **Model type:** Language Model
- **Language(s):** Turkish, English
- **Finetuned from model:** [Llama 2](https://huggingface.co/meta-llama/Llama-2-7b-hf)
- **Blog Post**: Will be released soon!

## Getting Started

### Loading in model with Hugging Face
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Turkish-Chat")
model = AutoModelForCausalLM.from_pretrained("sambanovasystems/SambaLingo-Turkish-Chat", device_map="auto", torch_dtype="auto")
```

### Suggested Inference Parameters
- Temperature: 0.8
- Repetition penalty: 1.0
- Top-p: 0.9

### Suggested Prompting

## Evaluation Results

## Training Details

## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
This model is intended for commercial and research use.


### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
SambaLingo should NOT be used for:

- Mission-critical applications
- Applications that involve the safety of others
- Making highly important decisions

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

Like all LLMs, SambaLingo has certain limitations:
- Hallucination: Model may sometimes generate responses that contain plausible-sounding but factually incorrect or irrelevant information.
- Code Switching: The model might unintentionally switch between languages or dialects within a single response, affecting the coherence and understandability of the output.
- Repetition: The Model may produce repetitive phrases or sentences, leading to less engaging and informative responses.
- Coding and Math: The model's performance in generating accurate code or solving complex mathematical problems may be limited.
- Toxicity: The model could inadvertently generate responses containing inappropriate or harmful content.

## Acknowledgments
We extend our heartfelt gratitude to the open-source AI community; this endeavor would not have been achievable without open source. SambaNova embraces the open-source community and aspires to actively contribute to this initiative.

We would like to give a special thanks to the following groups:
- Meta for open sourcing LLama 2 and open sourcing FLORES-200 dataset
- Nguyen et al for open sourcing CulturaX dataset
- CohereAI for their amazing work with AYA-101 and open sourcing a multilingual instruction tuning dataset
- EleutherAI for their open source evaluation framework
- Hugging Face-H4 team for open source the zephyr training recipe and alignment handbook repo


## Cite SambaLingo
```
@software{sambalingo,
  title = {{SambaLingo: Open Source Language Experts}},
  author = {SambaNova Systems},
  url = {https://huggingface.co/sambanovasystems/SambaLingo-Turkish-Chat}
  month = {2},
  year = {2024},
  version = {1.0},
}
```