SambaLingo-Serbian-Chat

SambaLingo-Serbian-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-Serbian-Chat is a human aligned chat model trained in Serbian and English. It is trained using direct preference optimization on top the base model SambaLingo-Serbian-Base. The base model adapts Llama-2-7b to Serbian by training on 17 billion tokens from the Serbian split of the Cultura-X dataset. Try this model at SambaLingo-chat-space.

Model Description

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• Developed by: SambaNova Systems
• Model type: Language Model
• Language(s): Serbian, English
• Finetuned from model: Llama-2-7b
• Try this model: SambaLingo-chat-space
• Paper: SambaLingo: Teaching Large Language Models New Languages
• Blog Post: sambalingo-open-source-language-experts

Getting Started

Loading Model With Hugging Face

Please make sure to set use_fast=False when loading the tokenizer.

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Serbian-Chat", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("sambanovasystems/SambaLingo-Serbian-Chat", device_map="auto", torch_dtype="auto")

Interacting With Model Pipeline

Please make sure to set use_fast=False when loading the tokenizer.

from transformers import pipeline
pipe = pipeline("text-generation", model="sambanovasystems/SambaLingo-Serbian-Chat", device_map="auto", use_fast=False)
messages = [
                {"role": "user", "content": {YOUR_QUESTION}},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt)[0]
outputs = outputs["generated_text"]

Suggested Inference Parameters

• Temperature: 0.8
• Repetition penalty: 1.0
• Top-p: 0.9

Prompting Guidelines

To prompt this model, please use the following chat template:

<|user|>\n{question}</s>\n<|assistant|>\n

Example Prompt and Generations

<|user|>
Od kada datiraju prva naselja na prostoru danasnjeg Beograda, i kada je grad osnovan?</s>
<|assistant|>
Beograd su naseljavali ljudi još u praistoriji, a prva naselja datiraju iz neolitskih vremena, pre oko 7.000 godina. Međutim, grad Beograd nije osnovan upravo u to vreme, već u srednjem veku, kada su Srbi u 9. veku osnovali srednjovekovni grad Beograd, na mestu današnjeg Kalemegdana. Grad je kasnije pretrpeo razna oсвајања, razaranja i obnavljanja, da bi se na kraju razvio u značajnu evropsku metropolu.

Training Details

The alignment phase follows the recipe for Zephyr-7B, and comprises two stages: supervised fine-tuning (SFT) and Direct Performance Optimization (DPO).

The SFT phase was done on the ultrachat_200k dataset mixed with the Google translated version of the ultrachat_200k dataset. It was trained for one epoch with global batch size 512 and max sequence length 2048 tokens. We used a linear decay learning rate of 2e-5 and 10% warmup.

The DPO phase was done on the ultrafeedback dataset and cai-conversation-harmless dataset, mixed with 10% of the data Google translated. It was trained with global batch size 32 and for three epochs. We used a linear decay learning rate of 5e-7, 10% warmup and β=0.1 as the regularization factor for DPO.

Tokenizer Details

We extended the vocabulary of the base llama model from 32,000 tokens to 57,000 tokens by adding up to 25,000 non-overlapping tokens from the new language.

Evaluation

For evaluation results see our paper: SambaLingo: Teaching Large Language Models New Languages

Uses

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Direct Use

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Use of this model is governed by the Meta’s Llama 2 Community License Agreement. Please review and accept the license before downloading the model weights.

Out-of-Scope Use

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SambaLingo should NOT be used for:

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

Bias, Risks, and Limitations

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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 possible 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 releasing 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

@misc{csaki2024sambalingo,
      title={SambaLingo: Teaching Large Language Models New Languages}, 
      author={Zoltan Csaki and Bo Li and Jonathan Li and Qiantong Xu and Pian Pawakapan and Leon Zhang and Yun Du and Hengyu Zhao and Changran Hu and Urmish Thakker},
      year={2024},
      eprint={2404.05829},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}