metadata

language:
  - ja
tags:
  - japanese-stablelm
  - causal-lm
pipeline_tag: text-generation
license: apache-2.0

Japanese StableLM-3B-4E1T Instruct

Model Description

This is a 3B-parameter decoder-only Japanese language model fine-tuned on instruction-following datasets, built on top of the base model Japanese StableLM-3B-4E1T Base.

Usage

import torch
from transformers import LlamaTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stabilityai/japanese-stablelm-3b-4e1t-instruct")
model = AutoModelForCausalLM.from_pretrained(
  "stabilityai/japanese-stablelm-3b-4e1t-instruct",
  trust_remote_code=True,
  torch_dtype="auto",
)
model.eval()

if torch.cuda.is_available():
    model = model.to("cuda")

def build_prompt(user_query, inputs="", sep="\n\n### "):
    sys_msg = "以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。"
    p = sys_msg
    roles = ["指示", "応答"]
    msgs = [": \n" + user_query, ": \n"]
    if inputs:
        roles.insert(1, "入力")
        msgs.insert(1, ": \n" + inputs)
    for role, msg in zip(roles, msgs):
        p += sep + role + msg
    return p

# Infer with prompt without any additional input
user_inputs = {
    "user_query": "与えられたことわざの意味を小学生でも分かるように教えてください。",
    "inputs": "情けは人のためならず"
}
prompt = build_prompt(**user_inputs)

input_ids = tokenizer.encode(
    prompt, 
    add_special_tokens=False, 
    return_tensors="pt"
)

tokens = model.generate(
    input_ids.to(device=model.device),
    max_new_tokens=256,
    temperature=1,
    top_p=0.95,
    do_sample=True,
)

out = tokenizer.decode(tokens[0][input_ids.shape[1]:], skip_special_tokens=True).strip()
print(out)

Model Details

Developed by: Stability AI
Model type: Japanese StableLM-3B-4E1T Instruct model is an auto-regressive language model based on the transformer decoder architecture.
Language(s): Japanese
License: This model is licensed under Apache License, Version 2.0.
Contact: For questions and comments about the model, please email lm@stability.ai

Model Architecture

The model is a decoder-only transformer similar to the LLaMA (Touvron et al., 2023) architecture with the following modifications:

Parameters	Hidden Size	Layers	Heads	Sequence Length
2,795,443,200	2560	32	32	4096

Position Embeddings: Rotary Position Embeddings (Su et al., 2021) applied to the first 25% of head embedding dimensions for improved throughput following Black et al. (2022).
Normalization: LayerNorm (Ba et al., 2016) with learned bias terms as opposed to RMSNorm (Zhang & Sennrich, 2019).
Tokenizer: GPT-NeoX (Black et al., 2022).

Training Datasets

Use and Limitations

Intended Use

The model is intended to be used by all individuals as a foundational model for application-specific fine-tuning without strict limitations on commercial use.

Limitations and bias

The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters which can be reflected in the model-generated text. We recommend users exercise reasonable caution when using these models in production systems. Do not use the model for any applications that may cause harm or distress to individuals or groups.

Acknowledgements

We are grateful for the contributions of the EleutherAI Polyglot-JA team in helping us to collect a large amount of pre-training data in Japanese. Polyglot-JA members includes Hyunwoong Ko (Project Lead), Fujiki Nakamura (originally started this project when he commited to the Polyglot team), Yunho Mo, Minji Jung, KeunSeok Im, and Su-Kyeong Jang.

We are also appreciative of AI Novelist/Sta (Bit192, Inc.) and the numerous contributors from Stable Community Japan for assisting us in gathering a large amount of high-quality Japanese textual data for model training.