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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: myBit-Llama2-jp-127M-4 |
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results: [] |
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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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# myBit-Llama2-jp-127M-4 |
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This model has 127M parameters. |
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The model is a pre-trained Bit-Llama2 of Parameters with only 1 epoch on a Japanese dataset. |
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The dataset used is [range3/wiki40b-ja](https://huggingface.co/datasets/range3/wiki40b-ja). |
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- Loss: 2.9790 |
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## Model description |
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Github： [BitNet-b158](https://github.com/Hajime-Y/BitNet-b158) |
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More information about this model can be found in the following pages: |
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- [BitNet&BitNet b158の実装①](https://note.com/hatti8/n/nc6890e79a19a) |
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- [BitNet&BitNet b158の実装②](https://note.com/hatti8/n/ne94f7a7d46df) |
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## How to use |
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1. install the library |
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``` |
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!pip install mybitnet |
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!pip install -U accelerate transformers==4.38.2 |
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!pip install torch |
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``` |
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2. get model |
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``` |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_name = "HachiML/myBit-Llama2-jp-127M-4" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True) |
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print(model) |
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``` |
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3. inference |
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``` |
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prompt = "昔々あるところに、" |
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input_ids = tokenizer.encode( |
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prompt, |
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return_tensors="pt" |
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) |
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tokens = model.generate( |
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input_ids.to(device=model.device), |
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max_new_tokens=128, |
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) |
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out = tokenizer.decode(tokens[0], skip_special_tokens=True) |
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print(out) |
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``` |
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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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- [range3/wiki40b-ja](https://huggingface.co/datasets/range3/wiki40b-ja) |
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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: 0.0024 |
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- train_batch_size: 96 |
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- eval_batch_size: 96 |
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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: polynomial |
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- lr_scheduler_warmup_steps: 5000 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 4.8696 | 0.05 | 2000 | 3.8588 | |
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| 3.7027 | 0.1 | 4000 | 3.6106 | |
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| 3.5648 | 0.15 | 6000 | 3.5014 | |
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| 3.448 | 0.2 | 8000 | 3.4153 | |
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| 3.3884 | 0.25 | 10000 | 3.3650 | |
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| 3.3462 | 0.29 | 12000 | 3.3280 | |
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| 3.3155 | 0.34 | 14000 | 3.3053 | |
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| 3.2932 | 0.39 | 16000 | 3.2891 | |
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| 3.2762 | 0.44 | 18000 | 3.2673 | |
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| 3.2594 | 0.49 | 20000 | 3.2533 | |
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| 3.2432 | 0.54 | 22000 | 3.2398 | |
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| 3.2286 | 0.59 | 24000 | 3.2186 | |
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| 3.2083 | 0.64 | 26000 | 3.1957 | |
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| 3.1867 | 0.69 | 28000 | 3.1769 | |
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| 3.1676 | 0.74 | 30000 | 3.1568 | |
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| 3.14 | 0.79 | 32000 | 3.1286 | |
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| 3.114 | 0.83 | 34000 | 3.1006 | |
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| 3.0848 | 0.88 | 36000 | 3.0696 | |
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| 3.0511 | 0.93 | 38000 | 3.0301 | |
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| 3.005 | 0.98 | 40000 | 2.9790 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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