weblab-10b-instruction-sft
Overview
This repository provides a Japanese-centric multilingual GPT-NeoX model of 10 billion parameters.
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Library
The model was trained using code based on EleutherAI/gpt-neox.
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Model architecture
A 36-layer, 4864-hidden-size transformer-based language model.
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Pre-training
The model was trained on around 600B tokens from a mixture of the following corpora.
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Instruction-supervised-finetuning
The model was finetuned on a subset records from a mixture of the following dataset. Training epoch: 1.
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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
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Japanese benchmark : JGLUE 8-task (2023-08-27)
- We used Stability-AI/lm-evaluation-harness library for evaluation.
- The 8-task average accuracy is based on results of JCommonsenseQA-1.1, JNLI-1.1, MARC-ja-1.1, JSQuAD-1.1, jaqket_v2-0.2, xlsum_ja-1.0, xwinograd_ja, and mgsm-1.0.
- 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 3,3,3,2,1,1,0,5.
- special_tokens_map.json is modified to avoid errors during the evaluation of the second half benchmarks. As a result, the results of the first half benchmarks became slightly different.
model average jcommonsenseqa jnli marc_ja jsquad jaqket_v2 xlsum_ja xwinograd_ja mgsm weblab-10b-instruction-sft 59.11 74.62 66.56 95.49 78.34 63.32 20.57 71.95 2 weblab-10b 50.74 66.58 53.74 82.07 62.94 56.19 10.03 71.95 2.4 -
Japanese benchmark : JGLUE 4-task (2023-08-18)
- 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-instruction-sft")
model = AutoModelForCausalLM.from_pretrained("matsuo-lab/weblab-10b-instruction-sft", torch_dtype=torch.float16)
if torch.cuda.is_available():
model = model.to("cuda")
text = "大規模言語モデルについて説明してください。"
text = f'以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。\n\n### 指示:\n{text}\n\n### 応答:'
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=0.7,
top_p=0.95
)
output = tokenizer.decode(output_ids.tolist()[0])
print(output)
