--- language: - ko library_name: transformers pipeline_tag: text-generation license: cc-by-nc-sa-4.0 datasets: - kyujinpy/KOR-OpenOrca-Platypus-v3 --- # **PracticeLLM/KoSOLAR-Platypus-10.7B** ## Model Details **Model Developers** Kyujin Han (kyujinpy) **Method** LoRA with quantization. **Base Model** [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2) **Dataset** [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3). **Hyperparameters** ``` python finetune.py \ --base_model yanolja/KoSOLAR-10.7B-v0.2 \ --data-path kyujinpy/KOR-OpenOrca-Platypus-v3 \ --output_dir ./Ko-PlatypusSOLAR-10.7B \ --batch_size 64 \ --micro_batch_size 1 \ --num_epochs 5 \ --learning_rate 2e-5 \ --cutoff_len 2048 \ --val_set_size 0 \ --lora_r 64 \ --lora_alpha 64 \ --lora_dropout 0.05 \ --lora_target_modules '[embed_tokens, q_proj, k_proj, v_proj, o_proj, gate_proj, down_proj, up_proj, lm_head]' \ --train_on_inputs False \ --add_eos_token False \ --group_by_length False \ --prompt_template_name en_simple \ --lr_scheduler 'cosine' \ ``` > Share all of things. It is my belief. # **Model Benchmark** ## Open Ko-LLM leaderboard & lm-evaluation-harness(zero-shot) - Follow up as [Ko-link](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard). | Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Ko-CommonGenV2 | | --- | --- | --- | --- | --- | --- | --- | | PracticeLLM/KoSOLAR-Platypus-10.7B | --- | --- | --- | --- | --- | --- | | [LDCC/LDCC-SOLAR-10.7B](https://huggingface.co/LDCC/LDCC-SOLAR-10.7B) | 59.34 | 55.38 | 65.56 | 53.38 | 64.39 | 57.97 | | [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2) | 55.62 | 50.51 | 62.29 | 53.76 | 47.31 | 64.23 | | [megastudyedu/M-SOLAR-10.7B-v1.3](https://huggingface.co/megastudyedu/M-SOLAR-10.7B-v1.3) | 56.64 | 51.37 | 60.93 | 54.91 | 48.45 | 67.53 | # Implementation Code ```python ### KO-Platypus from transformers import AutoModelForCausalLM, AutoTokenizer import torch repo = "PracticeLLM/KoSOLAR-Platypus-10.7B" OpenOrca = AutoModelForCausalLM.from_pretrained( repo, return_dict=True, torch_dtype=torch.float16, device_map='auto' ) OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo) ```