Japanese Stable LM 2 Base 1.6B

A beautiful anime-like hummingbird flying with the text "Japanese Stable LM 2" below it, with a lofi anime landscape of Mount Fuji forming the outline of the text "Japanese Stable LM 2" — Stable Diffusion 3

Please note: For commercial use, please refer to https://stability.ai/membership

Model Description

Japanese Stable LM 2 Base 1.6B is a 1.6B-parameter decoder-only language model based on Stable LM 2 1.6B that has been fine-tuned on a diverse collection of Japanese data, with the intent of maximizing downstream performance on Japanese language tasks.

For an instruction-following model, check Japanese Stable LM 2 Instruct 1.6B.

Usage

Get started generating text with Japanese Stable LM 2 Base 1.6B by using the following code snippet:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "stabilityai/japanese-stablelm-2-base-1_6b"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# The next line may need to be modified depending on the environment
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.float16, 
    low_cpu_mem_usage=True, 
    device_map="auto",
    trust_remote_code=True,
)

prompt = """
AI で科学研究を加速するには、
""".strip()

inputs = tokenizer(
    prompt,
    add_special_tokens=True,
    return_tensors="pt"
).to(model.device)

# this is for reproducibility.
# feel free to change to get different result
seed = 23
torch.manual_seed(seed)

tokens = model.generate(
    **inputs,
    max_new_tokens=128,
    temperature=0.99,
    top_p=0.95,
    do_sample=True,
)

out = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(out)

We suggest playing with different generation config (top_p, repetition_penalty etc) to find the best setup for your tasks. For example, use higher temperature for roleplay task, lower temperature for reasoning.

Model Details

Model type: Japanese Stable LM 2 Base 1.6B models are auto-regressive language models based on the transformer decoder architecture.
Language(s): Japanese
License: See the LICENSE file.
Commercial License: to use this model commercially, please refer to https://stability.ai/membership
Contact: For technical questions and comments about the model, please join Stable Community Japan. For future announcements / information about Stability AI models, research, and events, please follow @StabilityAI_JP.

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
1,644,417,024	2048	24	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).
Biases: We remove all bias terms from the feed-forward networks and multi-head self-attention layers, except for the biases of the query, key, and value projections (Bai et al., 2023).
Tokenizer: We use Arcade100k, a BPE tokenizer extended from OpenAI's tiktoken.cl100k_base. We split digits into individual tokens following findings by Liu & Low (2023).

Training Dataset

A mixture of the following corpora was used for continued pre-training.

Use and Limitations

Intended Use

The model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications. For commercial use, please refer to https://stability.ai/membership.

Limitations and Bias

As a base model, this model may exhibit unreliable, unsafe, or other undesirable behaviors that must be corrected through evaluation and fine-tuning prior to deployment. 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 that users exercise caution when using these models in production systems. Do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others.

Authors

This model was developed by the Research & Development team at Stability AI Japan, and the development was led by Meng Lee (@leemeng) and Naoki Orii (@mrorii). The members of the team are as follows:

How to cite

@misc{JapaneseStableLM2Base1.6B, 
      url={[https://huggingface.co/stabilityai/japanese-stablelm-2-base-1_6b](https://huggingface.co/stabilityai/japanese-stablelm-base-2-1_6b)}, 
      title={Japanese Stable LM 2 Base 1.6B},
      author={Lee, Meng and Nakamura, Fujiki and McCann, Paul and Orii, Naoki and Shibui, Yusuke and Phung, Duy and Zhuravinskyi, Maksym and Mahan, Dakota and Chi, Jerry}
}

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japanese-stablelm-2-base-1_6b

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