Create README.md

README.md

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+This is the 4 bit quanitzed gguf model.
+
+Original model: https://huggingface.co/sambanovasystems/SambaLingo-Thai-Chat-70B
+
+
+---
+language:
+- th
+- en
+license: llama2
+datasets:
+- HuggingFaceH4/ultrachat_200k
+- HuggingFaceH4/ultrafeedback_binarized
+- HuggingFaceH4/cai-conversation-harmless
+
+---
+
+
+
+# SambaLingo-Thai-Chat-70B
+
+<img src="/sambanovasystems/SambaLingo-Thai-Chat-70B/resolve/main/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-Thai-Chat-70B is a human aligned chat model trained in Thai and English. It is trained using direct preference optimization on top the base model [SambaLingo-Thai-Base-70B](https://huggingface.co/sambanovasystems/SambaLingo-Thai-Base-70B). The base model adapts [Llama-2-70b](https://huggingface.co/meta-llama/Llama-2-70b-hf) to Thai by training on 26 billion tokens from the Thai 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).
+
+## Model Description
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** [SambaNova Systems](https://sambanova.ai/)
+- **Model type:** Language Model
+- **Language(s):** Thai, English
+- **Finetuned from model:** [Llama-2-70b](https://huggingface.co/meta-llama/Llama-2-70b-hf)
+- **Paper:** [SambaLingo: Teaching Large Language Models New Languages](https://arxiv.org/abs/2404.05829)
+- **Blog Post**: [sambalingo-open-source-language-experts](https://sambanova.ai/blog/sambalingo-open-source-language-experts)
+
+## Getting Started
+
+### Loading Model With Hugging Face
+Please make sure to set use_fast=False when loading the tokenizer.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Thai-Chat-70B", use_fast=False)
+model = AutoModelForCausalLM.from_pretrained("sambanovasystems/SambaLingo-Thai-Chat-70B", device_map="auto", torch_dtype="auto")
+```
+
+### Interacting With Model Pipeline
+Please make sure to set use_fast=False when loading the tokenizer.
+```python
+from transformers import pipeline
+pipe = pipeline("text-generation", model="sambanovasystems/SambaLingo-Thai-Chat-70B", 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|>
+ประเทศไทยช่วงเช้าเคารพธงชาติเมื่อไร</s>
+<|assistant|>
+ในประเทศไทย เวลาเคารพธงชาติคือเวลา 08.00 น. และ 18.00 น. ทุกวัน ประชาชนจะยืนตรงและร้องเพลงชาติในช่วงเวลาเหล่านี้เพื่อเป็นสัญลักษณ์ของความรักชาติและความเคารพต่อประเทศ
+```
+
+## Training Details
+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).
+
+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.
+
+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. 
+
+
+## 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](https://arxiv.org/abs/2404.05829)
+
+## 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. -->
+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.
+
+
+### 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 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}
+}
+```
+
+