losyer8's picture
Create README.md
5541839
|
raw
history blame
5.69 kB
metadata
license: apache-2.0
language:
  - en
  - ja
programming_language:
  - C
  - C++
  - C#
  - Go
  - Java
  - JavaScript
  - Lua
  - PHP
  - Python
  - Ruby
  - Rust
  - Scala
  - TypeScript
library_name: transformers
pipeline_tag: text-generation
inference: false

llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0

This repository provides large language models developed by LLM-jp, a collaborative project launched in Japan.

Pre-trained models
llm-jp-13b-v1.0
llm-jp-1.3b-v1.0
Checkpoints format: transformers (Megatron-DeepSpeed format available here)

Required Libraries and Their Versions

  • torch>=2.0.0
  • transformers>=4.34.0
  • tokenizers>=0.14.0

Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("llm-jp/[Model_Name]")
model = AutoModelForCausalLM.from_pretrained("llm-jp/[Model_Name]", torch_dtype=torch.float16)
text = "自然言語処理とは何か"
text = text + "### 回答:"
tokenized_input = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt").to(model.device)
with torch.no_grad():
    output = model.generate(
        tokenized_input,
        max_new_tokens=100,
        do_sample=True,
        top_p=0.95,
        temperature=0.7,
    )[0]
print(tokenizer.decode(output))

Model Details

  • Model type: Transformer-based Language Model
  • Total seen tokens: 270B+
Model Params Layers Hidden size Heads Context length
13b model 13b 40 5120 40 2048
1.3b model 1.3b 24 2048 16 2048

Training

Tokenizer

The tokenizer of this model is based on huggingface/tokenizers Unigram byte-fallback model. The vocab entries were converted from llm-jp-tokenizer v2.1 (50k). Please refer to README.md of llm-ja-tokenizer for the details of vocab constuction steps.

  • Model: Hugging Face Fast Tokenizer using Unigram byte-fallback model which requires tokenizers>=0.14.0
  • Training algorithm: SentencePiece Unigram byte-fallback
  • Training data: A subset of the datasets for model pre-training
  • Vocabulary size: 50,570 (mixed vocabulary of Japanese, English, and source code)

Datasets

Pre-training

The models have been pre-trained on approximately 287.5B tokens, sourced from a blend of the following datasets.

Language Dataset Tokens
Japanese Wikipedia 1.5B
mC4 136B
English Wikipedia 5B
The Pile 135B
Codes The Stack 10B

Pretraining was done by 10-hold shards that consists approx. 27-28B tokens. We further finalized the pretraining with additional cleaned 27B tokens data.

Instruction tuning

The models have been fine-tuned on the following datasets.

Language Dataset description
Japanese jaster An automatically transformed data from the existing Japanese NLP datasets
databricks-dolly-15k A translated one by DeepL in LLM-jp
OpenAssistant Conversations Dataset A translated one by DeepL in LLM-jp

Evaluation

You can view the evaluation results of several LLMs on this leaderboard. We used llm-jp-eval for the evaluation.

Risks and Limitations

The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.

Send Questions to

llm-jp(at)nii.ac.jp

License

Apache License, Version 2.0

Model Card Authors

The names are listed in alphabetical order.

Namgi Han, Hirokazu Kiyomaru, Hiroshi Matsuda, Shota Sasaki, Shuhei Kurita, Taishi Nakamura, Takumi Okamoto.