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--- |
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language: |
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- ko |
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datasets: |
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- kyujinpy/KOR-OpenOrca-Platypus-v3 |
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library_name: transformers |
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pipeline_tag: text-generation |
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license: cc-by-nc-sa-4.0 |
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--- |
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# **⭐My custom LLM 13B⭐** |
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## Model Details |
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**Model Developers** |
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- Kyujin Han (kyujinpy) |
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**Model Architecture** |
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- My custom LLM 13B is an auto-regressive language model based on the LLaMA2 transformer architecture. |
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**Base Model** |
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- [beomi/llama-2-koen-13b](https://huggingface.co./beomi/llama-2-koen-13b) |
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**Training Dataset** |
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- [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co./datasets/kyujinpy/KOR-OpenOrca-Platypus-v3). |
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--- |
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# Model comparisons |
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> Ko-LLM leaderboard(11/27; [link](https://huggingface.co./spaces/upstage/open-ko-llm-leaderboard)) |
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| Model | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 | |
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| --- | --- | --- | --- | --- | --- | --- | |
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| ⭐My custom LLM 13B-v1⭐ | **50.19** | **45.99** | 56.93 | 41.78 | 41.66 | **64.58** | |
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| ⭐My custom LLM 13B-v2⭐ | 48.28 | 45.73 | 56.97 | 38.77 | 38.75 | 61.16 | |
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| **⭐My custom LLM 13B-v4⭐** | 49.89 | 45.05 | **57.06** | **41.83** | **42.93** | 62.57 | |
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--- |
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# Model comparisons2 |
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> AI-Harness evaluation; [link](https://github.com/Beomi/ko-lm-evaluation-harness) |
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| Model | Copa | Copa | HellaSwag | HellaSwag | BoolQ | BoolQ | Sentineg | Sentineg | |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | |
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| | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot | |
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| ⭐My custom LLM 13B-v1⭐ | 0.7987 | 0.8269 | 0.4994 | 0.5660 | **0.3343** | 0.5060 | 0.6984 | 0.9723 | |
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| ⭐My custom LLM 13B-v2⭐ | 0.7938 | 0.8209 | 0.4978 | 0.4893 | **0.3343** | 0.5614 | 0.6283 | 0.9773 | |
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| **⭐My custom LLM 13B-v4⭐** | **0.7988** | 0.8279 | **0.4995** | 0.4953 | **0.3343** | 0.3558 | **0.7825** | 0.9698 | |
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| [beomi/llama-2-koen-13b](https://huggingface.co./beomi/llama-2-koen-13b) | 0.7768 | 0.8128 | 0.4999 | 0.5127 | 0.3988 | 0.7038 | 0.5870 | 0.9748 | |
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--- |
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# Implementation Code |
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```python |
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### KO-Platypus |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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repo = "PracticeLLM/Custom-KoLLM-13B-v4" |
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OpenOrca = AutoModelForCausalLM.from_pretrained( |
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repo, |
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return_dict=True, |
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torch_dtype=torch.float16, |
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device_map='auto' |
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) |
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OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo) |
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``` |
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--- |
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# Hyperparameters |
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- learning_rate: 4e-4 |
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- batch_size: 16 |
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- epoch: 1 |
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- lora_target_modules: [gate_proj, down_proj, up_proj, q_proj, k_proj, v_proj, o_proj] |
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- cutoff_len: 4096 |
