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library_name: transformers
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---
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## Model Details
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### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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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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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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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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##
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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[More Information Needed]
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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## Model Card Authors [optional]
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library_name: transformers
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license: cc-by-nc-4.0
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language:
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- en
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- ko
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base_model:
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- upstage/SOLAR-10.7B-v1.0
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<a href="https://github.com/nlpai-lab/KULLM">
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<img src="kullm_logo.png" width="15%"/>
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</a>
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# KULLM3
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Introducing KULLM3, a model with advanced instruction-following and fluent chat abilities.
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It has shown remarkable performance in instruction-following, speficially by closely following gpt-3.5-turbo.
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To our knowledge, It is one of the best publicly opened Korean-speaking language models.
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For details, visit the [KULLM repository](https://github.com/nlpai-lab/KULLM)
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### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub.
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- **Developed by:** [NLP&AI Lab](http://nlp.korea.ac.kr/)
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- **Language(s) (NLP):** Korean, English
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- **License:** CC-BY-NC 4.0
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- **Finetuned from model:** [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
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## Example code
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### Install Dependencies
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```bash
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pip install torch transformers==4.38.2 accelerate
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```
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- In transformers>=4.39.0, generate() does not work well. (as of 2024.4.4.)
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### Python code
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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MODEL_DIR = "nlpai-lab/KULLM3"
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model = AutoModelForCausalLM.from_pretrained(MODEL_DIR, torch_dtype=torch.float16).to("cuda")
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tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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s = "고려대학교에 대해서 알고 있니?"
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conversation = [{'role': 'user', 'content': s}]
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inputs = tokenizer.apply_chat_template(
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conversation,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors='pt').to("cuda")
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_ = model.generate(inputs, streamer=streamer, max_new_tokens=1024)
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# 네, 고려대학교에 대해 알고 있습니다. 고려대학교는 대한민국 서울에 위치한 사립 대학교로, 1905년에 설립되었습니다. 이 대학교는 한국에서 가장 오래된 대학 중 하나로, 다양한 학부 및 대학원 프로그램을 제공합니다. 고려대학교는 특히 법학, 경제학, 정치학, 사회학, 문학, 과학 분야에서 높은 명성을 가지고 있습니다. 또한, 스포츠 분야에서도 활발한 활동을 보이며, 대한민국 대학 스포츠에서 중요한 역할을 하고 있습니다. 고려대학교는 국제적인 교류와 협력에도 적극적이며, 전 세계 다양한 대학과의 협력을 통해 글로벌 경쟁력을 강화하고 있습니다.
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```
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## Training Details
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### Training Data
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- [vicgalle/alpaca-gpt4](https://huggingface.co/datasets/vicgalle/alpaca-gpt4)
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- Mixed Korean instruction data (gpt-generated, hand-crafted, etc)
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- About 66000+ examples used totally
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### Training Procedure
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- Trained with fixed system prompt below.
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```text
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당신은 고려대학교 NLP&AI 연구실에서 만든 AI 챗봇입니다.
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당신의 이름은 'KULLM'으로, 한국어로는 '구름'을 뜻합니다.
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당신은 비도덕적이거나, 성적이거나, 불법적이거나 또는 사회 통념적으로 허용되지 않는 발언은 하지 않습니다.
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사용자와 즐겁게 대화하며, 사용자의 응답에 가능한 정확하고 친절하게 응답함으로써 최대한 도와주려고 노력합니다.
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질문이 이상하다면, 어떤 부분이 이상한지 설명합니다. 거짓 정보를 발언하지 않도록 주의합니다.
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```
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## Evaluation
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- Evaluation details such as testing data, metrics are written in [github](https://github.com/nlpai-lab/KULLM).
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- Without system prompt used in training phase, KULLM would show lower performance than expect.
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### Results
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<img src="kullm3_instruction_evaluation.png" width=60%>
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## Citation
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```text
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@misc{kullm,
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author = {NLP & AI Lab and Human-Inspired AI research},
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title = {KULLM: Korea University Large Language Model Project},
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year = {2023},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/nlpai-lab/kullm}},
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}
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```
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