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SambaLingo-Russian-Chat - GGUF

• Model creator: https://huggingface.co/sambanovasystems/
• Original model: https://huggingface.co/sambanovasystems/SambaLingo-Russian-Chat/

Name	Quant method	Size
SambaLingo-Russian-Chat.Q2_K.gguf	Q2_K	2.47GB
SambaLingo-Russian-Chat.Q3_K_S.gguf	Q3_K_S	2.87GB
SambaLingo-Russian-Chat.Q3_K.gguf	Q3_K	3.19GB
SambaLingo-Russian-Chat.Q3_K_M.gguf	Q3_K_M	3.19GB
SambaLingo-Russian-Chat.Q3_K_L.gguf	Q3_K_L	3.47GB
SambaLingo-Russian-Chat.IQ4_XS.gguf	IQ4_XS	3.53GB
SambaLingo-Russian-Chat.Q4_0.gguf	Q4_0	3.7GB
SambaLingo-Russian-Chat.IQ4_NL.gguf	IQ4_NL	3.72GB
SambaLingo-Russian-Chat.Q4_K_S.gguf	Q4_K_S	3.73GB
SambaLingo-Russian-Chat.Q4_K.gguf	Q4_K	3.94GB
SambaLingo-Russian-Chat.Q4_K_M.gguf	Q4_K_M	3.94GB
SambaLingo-Russian-Chat.Q4_1.gguf	Q4_1	4.09GB
SambaLingo-Russian-Chat.Q5_0.gguf	Q5_0	4.48GB
SambaLingo-Russian-Chat.Q5_K_S.gguf	Q5_K_S	4.48GB
SambaLingo-Russian-Chat.Q5_K.gguf	Q5_K	4.6GB
SambaLingo-Russian-Chat.Q5_K_M.gguf	Q5_K_M	4.6GB
SambaLingo-Russian-Chat.Q5_1.gguf	Q5_1	4.87GB
SambaLingo-Russian-Chat.Q6_K.gguf	Q6_K	5.31GB
SambaLingo-Russian-Chat.Q8_0.gguf	Q8_0	6.88GB

Original model description:

license: llama2 datasets:

• HuggingFaceH4/ultrachat_200k
• HuggingFaceH4/ultrafeedback_binarized
• HuggingFaceH4/cai-conversation-harmless language:
• ru
• en

---

SambaLingo-Russian-Chat

SambaLingo-Russian-Chat is a human aligned chat model trained in Russian and English. It is trained using direct preference optimization on top the base model SambaLingo-Russian-Base. The base model adapts Llama-2-7b to Russian by training on 63 billion tokens from the Russian split of the Cultura-X dataset. Try this model at SambaLingo-chat-space.

Model Description

• Developed by: SambaNova Systems
• Model type: Language Model
• Language(s): Russian, 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-Russian-Chat", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("sambanovasystems/SambaLingo-Russian-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-Russian-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 Prompts and Generations

<|user|>
Завершите параграф: Родион Раскольников - главный персонаж романа "Преступление и наказание" Федора Достоевского. Его характер очень сложен и     эволюционирует на протяжении всей истории. Он обладает выдающимися умственными способностями и амбициями, но</s>
<|assistant|>
его внутренняя бор    ьба и моральные сомнения мешают ему действовать в соответствии со своими желаниями. Раскольников — бедный студент, который терпит финансовые трудности и чувствует себ    я бессильным изменить свою жизнь. Он приходит к теории, согласно которой некоторые люди имеют право совершать преступления ради достижения великих целей, и решает про    верить свою теорию, убив старуху-процентщицу. Однако его действия приводят к глубокому чувству вины и психологическому срыву, который заставляет его искать искупления     и искупления. На протяжении всего романа Раскольников проходит путь самопознания и личностного роста, осознавая важность сочувствия, сострадания и ответственности пе    ред другими. В конечном итоге он принимает на себя ответственность за свои действия и находит искупление через раскаяние и прощение.

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

Direct Use

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

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 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}
}