--- tags: - text-generation license: cc-by-nc-4.0 language: - ko base_model: beomi/OPEN-SOLAR-KO-10.7B pipeline_tag: text-generation --- # **DataVortexS-10.7B-dpo-v1.1** DataVortex ## Our Team | Research & Engineering | Product Management | | :--------------------: | :----------------: | | Kwangseok Yang | Seunghyun Choi | | Jeongwon Choi | Hyoseok Choi | ## **Model Details** ### **Base Model** [beomi/OPEN-SOLAR-KO-10.7B](https://huggingface.co/beomi/OPEN-SOLAR-KO-10.7B) ### **Trained On** - **OS**: Ubuntu 22.04 - **GPU**: H100 80GB 4ea - **transformers**: v4.36.2 ### **Instruction format** It follows **Alpaca (Chat)** format. E.g. ```python text = """\ ### System: 당신은 사람들이 정보를 찾을 수 있도록 도와주는 인공지능 비서입니다. ### User: 대한민국의 수도는 어디야? ### Assistant: 대한민국의 수도는 서울입니다. ### User: 서울 인구는 총 몇 명이야? """ ``` ## **Model Benchmark** ### **[Ko LM Eval Harness](https://github.com/Beomi/ko-lm-evaluation-harness)** | Task | 0-shot | 5-shot | 10-shot | 50-shot | | :--------------- | -----------: | -----------: | -----------: | -----------: | | kobest_boolq | 0.915201 | 0.908687 | 0.912913 | 0.912913 | | kobest_copa | 0.815575 | 0.858739 | 0.865826 | 0.876896 | | kobest_hellaswag | 0.510673 | 0.515149 | 0.517118 | 0.517941 | | kobest_sentineg | 0.3517 | 0.977329 | 0.992443 | 0.984886 | | **Average** | **0.648287** | **0.814976** | **0.822075** | **0.823159** | ### **[Ko-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)** On Benchmarking ... | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 | | ------: | -----: | -----------: | ------: | ------------: | --------------: | | 0 | 0 | 0 | 0 | 0 | 0 | ## **Implementation Code** This model contains the chat_template instruction format. You can use the code below. ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.1") tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.1") messages = [ {"role": "system", "content": "당신은 사람들이 정보를 찾을 수 있도록 도와주는 인공지능 비서입니다."}, {"role": "user", "content": "대한민국의 수도는 어디야?"}, {"role": "assistant", "content": "대한민국의 수도는 서울입니다."}, {"role": "user", "content": "서울 인구는 총 몇 명이야?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## **License** This model is licensed under the [cc-by-nc-4.0](https://creativecommons.org/licenses/by-nc/4.0/). which allows others to share and adapt the model for non-commercial purposes.
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