--- tags: - text-generation license: cc-by-nc-sa-4.0 language: - ko base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 pipeline_tag: text-generation datasets: - beomi/KoAlpaca-v1.1a - jojo0217/korean_rlhf_dataset - kyujinpy/OpenOrca-KO - nlpai-lab/kullm-v2 --- # **DataVortexTL-1.1B-v0.1** ## **Model Details** ### **Base Model** [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) ### **Trained On** - **OS**: Ubuntu 20.04 - **GPU**: H100 80GB 1ea - **transformers**: v4.36.2 ### **Dataset** - [beomi/KoAlpaca-v1.1a](https://huggingface.co/datasets/beomi/KoAlpaca-v1.1a) - [jojo0217/korean_rlhf_dataset](https://huggingface.co/datasets/jojo0217/korean_rlhf_dataset) - [kyujinpy/OpenOrca-KO](https://huggingface.co/datasets/kyujinpy/OpenOrca-KO) - [nlpai-lab/kullm-v2](https://huggingface.co/datasets/nlpai-lab/kullm-v2) ### **Instruction format** It follows **TinyLlama** 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-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)** On Benchmarking ... | Model | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 | | ---------------------------- | ------- | ------ | ------------ | ------- | ------------- | --------------- | | DataVortexM-7B-Instruct-v0.1 | 39.81 | 34.13 | 42.35 | 38.73 | 45.46 | 38.37 | | DataVortexS-10.7B-v0.1 | 0 | 0 | 0 | 0 | 0 | 0 | | DataVortexS-10.7B-v0.2 | 43.6 | 38.74 | 50.74 | 38.98 | 44.7 | 44.86 | | DataVortexS-10.7B-v0.3 | 0 | 0 | 0 | 0 | 0 | 0 | | DataVortexS-10.7B-v0.4 | 0 | 0 | 0 | 0 | 0 | 0 | | DataVortexS-10.7B-v0.4 | 0 | 0 | 0 | 0 | 0 | 0 | | **DataVortexTL-1.1B-v0.1** | **0** | **0** | **0** | **0** | **0** | **0** | | DataVortexS-10.7B-dpo-v0.1 | 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/DataVortexTL-1.1B-v0.1") tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexTL-1.1B-v0.1") 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** The model is licensed under the [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license, which allows others to copy, modify, and share the work non-commercially, as long as they give appropriate credit and distribute any derivative works under the same license.