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:

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

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
Downloads last month
15
Safetensors
Model size
128M params
Tensor type
F32
·
Inference Examples
Inference API (serverless) does not yet support model repos that contain custom code.