|
--- |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: myBit-Llama2-jp-127M-4 |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# myBit-Llama2-jp-127M-4 |
|
|
|
This model has 127M parameters. |
|
The model is a pre-trained Bit-Llama2 of Parameters with only 1 epoch on a Japanese dataset. |
|
The dataset used is [range3/wiki40b-ja](https://huggingface.co/datasets/range3/wiki40b-ja). |
|
- Loss: 2.9790 |
|
|
|
## Model description |
|
|
|
Github： [BitNet-b158](https://github.com/Hajime-Y/BitNet-b158) |
|
More information about this model can be found in the following pages: |
|
[BitNet&BitNet b158の実装](https://note.com/hatti8/n/nc6890e79a19a) |
|
|
|
## How to use |
|
|
|
1. install the library |
|
``` |
|
!pip install mybitnet |
|
!pip install -U accelerate transformers==4.38.2 |
|
!pip install torch |
|
``` |
|
|
|
2. get model |
|
``` |
|
import torch |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
|
|
model_name = "HachiML/myBit-Llama2-jp-127M-4" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True) |
|
print(model) |
|
``` |
|
|
|
3. inference |
|
``` |
|
prompt = "昔々あるところに、" |
|
input_ids = tokenizer.encode( |
|
prompt, |
|
return_tensors="pt" |
|
) |
|
tokens = model.generate( |
|
input_ids.to(device=model.device), |
|
max_new_tokens=128, |
|
) |
|
|
|
out = tokenizer.decode(tokens[0], skip_special_tokens=True) |
|
print(out) |
|
``` |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
- [range3/wiki40b-ja](https://huggingface.co/datasets/range3/wiki40b-ja) |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 0.0024 |
|
- train_batch_size: 96 |
|
- eval_batch_size: 96 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: polynomial |
|
- lr_scheduler_warmup_steps: 5000 |
|
- num_epochs: 1 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:-----:|:---------------:| |
|
| 4.8696 | 0.05 | 2000 | 3.8588 | |
|
| 3.7027 | 0.1 | 4000 | 3.6106 | |
|
| 3.5648 | 0.15 | 6000 | 3.5014 | |
|
| 3.448 | 0.2 | 8000 | 3.4153 | |
|
| 3.3884 | 0.25 | 10000 | 3.3650 | |
|
| 3.3462 | 0.29 | 12000 | 3.3280 | |
|
| 3.3155 | 0.34 | 14000 | 3.3053 | |
|
| 3.2932 | 0.39 | 16000 | 3.2891 | |
|
| 3.2762 | 0.44 | 18000 | 3.2673 | |
|
| 3.2594 | 0.49 | 20000 | 3.2533 | |
|
| 3.2432 | 0.54 | 22000 | 3.2398 | |
|
| 3.2286 | 0.59 | 24000 | 3.2186 | |
|
| 3.2083 | 0.64 | 26000 | 3.1957 | |
|
| 3.1867 | 0.69 | 28000 | 3.1769 | |
|
| 3.1676 | 0.74 | 30000 | 3.1568 | |
|
| 3.14 | 0.79 | 32000 | 3.1286 | |
|
| 3.114 | 0.83 | 34000 | 3.1006 | |
|
| 3.0848 | 0.88 | 36000 | 3.0696 | |
|
| 3.0511 | 0.93 | 38000 | 3.0301 | |
|
| 3.005 | 0.98 | 40000 | 2.9790 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.38.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
