license: llama2
datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
language:
- tr
- en
SambaLingo-Turkish-Chat
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. The base model adapts Llama 2 to Turkish by training on 63 billion tokens from the Turkish split of the Cultura-X dataset.
Model Description
- Developed by: SambaNova Systems
- Model type: Language Model
- Language(s): Turkish, English
- Finetuned from model: Llama 2
- Blog Post: Will be released soon!
Getting Started
Loading in model with Hugging Face
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
Direct Use
This model is intended for commercial and research use.
Out-of-Scope Use
SambaLingo should NOT be used for:
- Mission-critical applications
- Applications that involve the safety of others
- Making highly important decisions
Bias, Risks, and 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},
}