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--- |
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datasets: |
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- heegyu/wizard_vicuna_70k_v2 |
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license: apache-2.0 |
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--- |
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Hyperparameters |
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- 3/8 epoch(3rd epoch checkpoing while 8epoch training) |
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- 1e-4 -> 1e-5 with cosine lr decay |
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- batch size 128 |
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- max sequence length 2048 |
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- AdamW(weigth decay=0.01, b1=0.9, b2=0.99, grad_clip=1.0) |
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- no warmup |
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- BF16 |
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- Base Model: [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) |
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``` |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("heegyu/WizardVicuna-open-llama-3b-v2") |
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model = AutoModelForCausalLM.from_pretrained("heegyu/WizardVicuna-open-llama-3b-v2") |
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inputs = tokenizer(["Human: Hi, nice to meet you!\n\nAssistant: "], return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=16) |
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print(tokenizer.batch_decode(outputs, skip_special_tokens=False)) |
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``` |
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output: `['Human: Hi, nice to meet you!\n\nAssistant: Hello. Great to meet you too. Well, how can I assist you today?<|endoftext|>']` |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_heegyu__WizardVicuna-open-llama-3b-v2) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 34.11 | |
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| ARC (25-shot) | 37.71 | |
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| HellaSwag (10-shot) | 66.6 | |
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| MMLU (5-shot) | 27.23 | |
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| TruthfulQA (0-shot) | 36.8 | |
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| Winogrande (5-shot) | 63.3 | |
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| GSM8K (5-shot) | 0.99 | |
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| DROP (3-shot) | 6.12 | |
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