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--- |
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tags: |
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- generated_from_trainer |
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- retnet |
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model-index: |
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- name: kakuyomu-retnet-300m-1 |
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results: [] |
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license: mit |
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language: |
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- ja |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# LightNovel-Intro-RetNet-400M |
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This model is a RetNet model trained from scratch using https://github.com/syncdoth/RetNet. |
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Demo: https://huggingface.co/spaces/isek-ai/LightNovel-Intro-RetNet-400M-Demo |
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## Usage |
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First install the required libraries: |
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``` |
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pip install transformers safetensors timm |
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``` |
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Then clone the repository of [implementation of RetNet written by syncdoth](https://github.com/syncdoth/RetNet) in the same directory as the inference script: |
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``` |
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git clone https://github.com/syncdoth/RetNet.git |
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``` |
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Example inference script: |
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```py |
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig |
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MODEL_NAME = "isek-ai/LightNovel-Intro-RetNet-400M" |
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device = "cuda" |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
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model = AutoModelForCausalLM.from_pretrained( |
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MODEL_NAME, |
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trust_remote_code=True, |
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).to(device) |
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gen_config = GenerationConfig.from_pretrained(MODEL_NAME) |
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gen_config.max_new_tokens = 32 |
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inputs = tokenizer("็ฎใ่ฆใใใจใ", return_tensors="pt", add_special_tokens=False).to(device) |
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print("Generating...") |
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result = model.generate(**inputs, generation_config=gen_config) |
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print(tokenizer.decode(result[0], skip_special_tokens=True)) |
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# ็ฎใ่ฆใใใจใ่ฆ็ฅใใฌ็ฉบ้ใซๅฑ
ใใ ใใ......?ใ ๆใใใใใชๅฃฐใๅบใใใจใซ้ๅๆใๆใใใ็ขบใใๆฐไปใใฐ็งใฏ |
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``` |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0006 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 5.5155 | 0.06 | 1000 | 5.5331 | |
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| 5.0106 | 0.13 | 2000 | 5.1774 | |
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| 4.793 | 0.19 | 3000 | 4.9399 | |
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| 4.7078 | 0.26 | 4000 | 4.7737 | |
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| 4.4789 | 0.32 | 5000 | 4.6373 | |
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| 4.3269 | 0.38 | 6000 | 4.5422 | |
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| 4.337 | 0.45 | 7000 | 4.4632 | |
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| 4.374 | 0.51 | 8000 | 4.4070 | |
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| 4.1447 | 0.58 | 9000 | 4.3293 | |
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| 4.1402 | 0.64 | 10000 | 4.2881 | |
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| 4.1329 | 0.7 | 11000 | 4.2287 | |
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| 3.9985 | 0.77 | 12000 | 4.1858 | |
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| 4.1185 | 0.83 | 13000 | 4.1506 | |
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| 4.0515 | 0.9 | 14000 | 4.0993 | |
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| 3.9984 | 0.96 | 15000 | 4.0611 | |
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| 3.7731 | 1.02 | 16000 | 4.0423 | |
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| 3.7403 | 1.09 | 17000 | 3.8166 | |
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| 3.6778 | 1.15 | 18000 | 3.8000 | |
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| 3.7227 | 1.22 | 19000 | 3.7875 | |
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| 3.6051 | 1.28 | 20000 | 3.7664 | |
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| 3.6143 | 1.34 | 21000 | 3.7496 | |
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| 3.6323 | 1.41 | 22000 | 3.7278 | |
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| 3.6487 | 1.47 | 23000 | 3.7089 | |
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| 3.6524 | 1.54 | 24000 | 3.6951 | |
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| 3.5621 | 1.6 | 25000 | 3.6801 | |
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| 3.5722 | 1.66 | 26000 | 3.6708 | |
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| 3.5277 | 1.73 | 27000 | 3.6635 | |
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| 3.6224 | 1.79 | 28000 | 3.6565 | |
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| 3.5663 | 1.85 | 29000 | 3.6532 | |
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| 3.5937 | 1.92 | 30000 | 3.6515 | |
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| 3.5944 | 1.98 | 31000 | 3.6510 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
