---
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①](https://note.com/hatti8/n/nc6890e79a19a)
 - [BitNet&BitNet b158の実装②](https://note.com/hatti8/n/ne94f7a7d46df)

## How to use

1. install the library
```
!pip install mybitnet==0.2.3
!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