myBit-Llama2-jp-127M-4

<!-- 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.

• Loss: 2.9790

Model description

Github：　BitNet-b158
More information about this model can be found in the following pages:

• BitNet&BitNet b158の実装①
• BitNet&BitNet b158の実装②

How to use

1. install the library

!pip install mybitnet==0.2.3
!pip install -U accelerate transformers==4.38.2
!pip install torch

1. 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)

1. 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

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

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2