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