metadata
license: cc-by-nc-4.0
weblab-10b
Overview
This repository provides a Japanese-centric multilingual GPT-NeoX model of 10 billion parameters.
Library
The model was trained using code based on EleutherAI/gpt-neox.
Model architecture
A 36-layer, 4864-hidden-size transformer-based language model.
Pre-training
The model was trained on around 600B tokens from a mixture of the following corpora
Model Series
Variant Link weblab-10b-instruction-sft https://huggingface.co/matsuo-lab/weblab-10b-instruction-sft weblab-10b https://huggingface.co/matsuo-lab/weblab-10b Authors
Takeshi Kojima
Benchmarking
Japanese benchmark
- We used Stability-AI/lm-evaluation-harness library for evaluation.
- The 4-task average accuracy is based on results of JCommonsenseQA-1.1, JNLI-1.1, MARC-ja-1.1, and JSQuAD-1.1.
- model loading is performed with float16, and evaluation is performed with template version 0.3 using the few-shot in-context learning.
- The number of few-shots is 3,3,3,2.
Model Average JCommonsenseQA JNLI MARC-ja JSQuAD weblab-10b-instruction-sft 78.78 74.35 65.65 96.06 79.04 weblab-10b 66.38 65.86 54.19 84.49 60.98
How to use the model
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("matsuo-lab/weblab-10b")
model = AutoModelForCausalLM.from_pretrained("matsuo-lab/weblab-10b")
if torch.cuda.is_available():
model = model.to("cuda")
text = "吾輩は猫である。"
token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")
with torch.no_grad():
output_ids = model.generate(
token_ids.to(model.device),
max_new_tokens=100,
do_sample=True,
temperature=0.6,
top_p=0.95,
pad_token_id=tokenizer.pad_token_id,
bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.eos_token_id
)
output = tokenizer.decode(output_ids.tolist()[0])
print(output)