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Model: sambanovasystems/SambaLingo-Turkish-Chat Source: Original Platform
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README.md
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README.md
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---
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license: llama2
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datasets:
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- HuggingFaceH4/ultrachat_200k
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- HuggingFaceH4/ultrafeedback_binarized
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- HuggingFaceH4/cai-conversation-harmless
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language:
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- tr
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- en
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---
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# SambaLingo-Turkish-Chat
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<img src="SambaLingo_Logo.png" width="340" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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<!-- Provide a quick summary of what the model is/does. -->
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SambaLingo-Turkish-Chat is a human aligned chat model trained in 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-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf) to Turkish by training on 42 billion tokens from the Turkish split of the [Cultura-X](https://huggingface.co/datasets/uonlp/CulturaX) dataset. Try this model at [SambaLingo-chat-space](https://huggingface.co/spaces/sambanovasystems/SambaLingo-chat-space).
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## Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [SambaNova Systems](https://sambanova.ai/)
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- **Model type:** Language Model
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- **Language(s):** Turkish, English
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- **Finetuned from model:** [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf)
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- **Try this model:** [SambaLingo-chat-space](https://huggingface.co/spaces/sambanovasystems/SambaLingo-chat-space)
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- **Paper:** [SambaLingo: Teaching Large Language Models New Languages](https://arxiv.org/abs/2404.05829)
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- **Blog Post**: [sambalingo-open-source-language-experts](https://sambanova.ai/blog/sambalingo-open-source-language-experts)
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## Getting Started
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### Loading Model With Hugging Face
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Please make sure to set use_fast=False when loading the tokenizer.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Turkish-Chat", use_fast=False)
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model = AutoModelForCausalLM.from_pretrained("sambanovasystems/SambaLingo-Turkish-Chat", device_map="auto", torch_dtype="auto")
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```
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### Interacting With Model Pipeline
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Please make sure to set use_fast=False when loading the tokenizer.
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```python
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from transformers import pipeline
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pipe = pipeline("text-generation", model="sambanovasystems/SambaLingo-Turkish-Chat", device_map="auto", use_fast=False)
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messages = [
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{"role": "user", "content": {YOUR_QUESTION}},
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]
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prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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outputs = pipe(prompt)[0]
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outputs = outputs["generated_text"]
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```
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### Suggested Inference Parameters
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- Temperature: 0.8
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- Repetition penalty: 1.0
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- Top-p: 0.9
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### Prompting Guidelines
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To prompt this model, please use the following chat template:
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```
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<|user|>\n{question}</s>\n<|assistant|>\n
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```
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## Training Details
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The alignment phase follows the recipe for [Zephyr-7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), and comprises two stages: supervised fine-tuning (SFT) and Direct Performance Optimization (DPO).
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The SFT phase was done on the [ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/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.
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The DPO phase was done on the [ultrafeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) dataset and [cai-conversation-harmless](https://huggingface.co/datasets/HuggingFaceH4/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.
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## Tokenizer Details
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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.
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## Evaluation
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For evaluation results see our paper: [SambaLingo: Teaching Large Language Models New Languages](https://arxiv.org/abs/2404.05829)
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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Use of this model is governed by the Meta’s [Llama 2 Community License Agreement](https://ai.meta.com/llama/license/). Please review and accept the license before downloading the model weights.
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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SambaLingo should NOT be used for:
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- Mission-critical applications
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- Applications that involve the safety of others
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- Making highly important decisions
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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Like all LLMs, SambaLingo has certain limitations:
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- Hallucination: Model may sometimes generate responses that contain plausible-sounding but factually incorrect or irrelevant information.
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- Code Switching: The model might unintentionally switch between languages or dialects within a single response, affecting the coherence and understandability of the output.
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- Repetition: The Model may produce repetitive phrases or sentences, leading to less engaging and informative responses.
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- Coding and Math: The model's performance in generating accurate code or solving complex mathematical problems may be limited.
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- Toxicity: The model could inadvertently generate responses containing inappropriate or harmful content.
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## Acknowledgments
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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.
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We would like to give a special thanks to the following groups:
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- Meta for open sourcing LLama 2 and open sourcing FLORES-200 dataset
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- Nguyen et al for open sourcing CulturaX dataset
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- CohereAI for releasing AYA-101 and open sourcing a multilingual instruction tuning dataset
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- EleutherAI for their open source evaluation framework
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- Hugging Face-H4 team for open source the zephyr training recipe and alignment handbook repo
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## Cite SambaLingo
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```
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@misc{csaki2024sambalingo,
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title={SambaLingo: Teaching Large Language Models New Languages},
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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},
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year={2024},
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eprint={2404.05829},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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