--- library_name: transformers tags: - pytorch license: llama3 language: - ko pipeline_tag: text-generation ---
# Kor-LLAMA3 Model > Update @ 2024.06.05: First release of Llama3-Ocelot-8B-instruct-v01 This model card corresponds to the 8B Instruct version of the **Llama-Ko** model. The train wad done on A100-80GB **Resources and Technical Documentation**: * [llama Model](beomi/Llama-3-Open-Ko-8B) - [Orca-Math](https://huggingface.co/datasets/kuotient/orca-math-korean-dpo-pairs) - [ko_Ultrafeedback_binarized](maywell/ko_Ultrafeedback_binarized) **Citation** **Model Developers**: frcp, nebchi, pepperonipizza97 ## Model Information It is an LLM model capable of generating Korean text, trained on a pre-trained base model with high-quality Korean SFT dataset and DPO dataset. #### *Inputs and outputs* - **Input:** Text string, such as a question, a prompt, or a document to be summarized. - **Output:** Generated Korean-language text in response to the input, such as an answer to a question, or a summary of a document. #### Running the model on a single / multi GPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cpm-ai/Ocelot-Ko-self-instruction-10.8B-v1.0") model = AutoModelForCausalLM.from_pretrained("cpm-ai/Ocelot-Ko-self-instruction-10.8B-v1.0", device_map="auto") pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=4096, streamer=streamer) text = '대한민국의 수도는 어디인가요?' messages = [ { "role": "user", "content": "{}".format(text) } ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe( prompt, temperature=0.2, add_special_tokens=True ) print(outputs[0]["generated_text"][len(prompt):]) ``` ### results ```python 대한민국의 수도는 서울특별시입니다. 서울특별시에는 청와대, 국회의사당, 대법원 등 대한민국의 주요 정부기관이 위치해 있습니다. 또한 서울시는 대한민국의 경제, 문화, 교육, 교통의 중심지로써 대한민국의 수도이자 대표 도시입니다.제가 도움이 되었길 바랍니다. 더 궁금한 점이 있으시면 언제든지 물어보세요! ``` ```bibtex @misc {cpm-ai/Ocelot-Ko-self-instruction-10.8B-v1.0, author = { {frcp, nebchi, pepperonipizza97} }, title = { solar-kor-resume}, year = 2024, url = { https://huggingface.co/cpm-ai/Ocelot-Ko-self-instruction-10.8B-v1.0 }, publisher = { Hugging Face } } ``` Results in [LogicKor](https://github.com/StableFluffy/LogicKor)* are as follows: | Model | Single turn* | Multi turn* | Overall* | |:------------------------------:|:------------:|:-----------:|:--------:| | gemini-1.5-pro-preview-0215 | 7.90 | 6.26 | 7.08 | | xionic-1-72b-20240404 | 7.23 | 6.28 | 6.76 | | Ocelot-Instruct | 6.79 | **6.71** | 6.75 | | allganize/Llama-3-Alpha-Ko-8B-Instruct | 7.14 | 6.09 | 6.61 |