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license: cc-by-nc-4.0 |
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# weblab-10b |
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# Overview |
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This repository provides a Japanese-centric multilingual GPT-NeoX model of 10 billion parameters. |
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* **Library** |
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The model was trained using code based on [EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox). |
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* **Model architecture** |
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A 36-layer, 4864-hidden-size transformer-based language model. |
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* **Pre-training** |
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The model was trained on around **600B** tokens from a mixture of the following corpora |
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- [Japanese C4](https://huggingface.co/datasets/mc4) |
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- [The Pile](https://huggingface.co/datasets/EleutherAI/pile) |
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* **Model Series** |
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| Variant | Link | |
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| weblab-10b-instruction-sft | https://huggingface.co/matsuo-lab/weblab-10b-instruction-sft | |
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| weblab-10b | https://huggingface.co/matsuo-lab/weblab-10b | |
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* **Authors** |
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Takeshi Kojima |
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# Benchmarking |
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* **Japanese benchmark** |
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- *We used [Stability-AI/lm-evaluation-harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/2f1583c0735eacdfdfa5b7d656074b69577b6774) library for evaluation.* |
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- *The 4-task average accuracy is based on results of JCommonsenseQA-1.1, JNLI-1.1, MARC-ja-1.1, and JSQuAD-1.1.* |
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- *model loading is performed with float16, and evaluation is performed with template version 0.3 using the few-shot in-context learning.* |
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- *The number of few-shots is 3,3,3,2.* |
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| Model | Average | JCommonsenseQA | JNLI | MARC-ja | JSQuAD | |
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| :-- | :-- | :-- | :-- | :-- | :-- | |
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| weblab-10b-instruction-sft | 78.78 | 74.35 | 65.65 | 96.06 | 79.04 | |
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| weblab-10b | 66.38 | 65.86 | 54.19 | 84.49 | 60.98 | |
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# How to use the model |
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~~~~python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("matsuo-lab/weblab-10b") |
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model = AutoModelForCausalLM.from_pretrained("matsuo-lab/weblab-10b") |
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if torch.cuda.is_available(): |
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model = model.to("cuda") |
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text = "吾輩は猫である。" |
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token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt") |
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with torch.no_grad(): |
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output_ids = model.generate( |
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token_ids.to(model.device), |
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max_new_tokens=100, |
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do_sample=True, |
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temperature=0.7, |
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top_p=0.95, |
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pad_token_id=tokenizer.pad_token_id, |
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bos_token_id=tokenizer.bos_token_id, |
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eos_token_id=tokenizer.eos_token_id |
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) |
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output = tokenizer.decode(output_ids.tolist()[0]) |
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print(output) |
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~~~~ |
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# Licenese |
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[cc-by-nc-4.0](https://creativecommons.org/licenses/by-nc/4.0/) |
