PLaMo 2 1B

Model Description

PLaMo 2 1B is a 1B model pre-trained on English and Japanese datasets, developed by Preferred Elements, Inc.

PLaMo 2 1B is released under Apache License version 2.0.

NOTE: This model has NOT been instruction-tuned for chat dialog or other downstream tasks.

Usage

Requirements

numpy>=1.26.4
numba>=0.60.0
torch>=2.4.1
transformers>=4.44.2
mamba_ssm>=2.2.2
causal_conv1d>=1.4.0

Use a pipeline as a high-level helper

import transformers
pipeline = transformers.pipeline("text-generation", model="pfnet/plamo-2-1b", trust_remote_code=True)
print(pipeline("The future of artificial intelligence technology is ", max_new_tokens=32))

Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("pfnet/plamo-2-1b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("pfnet/plamo-2-1b", trust_remote_code=True)
text = "これからの人工知能技術は"
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_tokens = model.generate(
    inputs=input_ids,
    max_new_tokens=32,
    do_sample=True,
    top_k=50,
    top_p=0.95,
    temperature=1.0,
)[0]
generated_text = tokenizer.decode(generated_tokens)
print(generated_text)

Model Details

Model size: 1B
Trained tokens: 4T tokens
Developed by: Preferred Elements, Inc.
Model type: Causal decoder-only
Language(s): English, Japanese
License: Apache License version 2.0

Training Dataset

We trained PLaMo 2 1B in two phases, phase 1 with 3.5T tokens and phase 2 with 0.5T tokens. The percentage of datasets in each phase is shown in the following table.

	3.5T (phase 1)	0.5T (phase 2)	Tokens
English	45 %	35 %	1.75 T
Japanese	30 %	40 %	1.25 T
Coding	15 %	15 %	0.6 T
Other	10 %	10 %	0.4 T

Tokenizer

PLaMo 2 1B tokenizer is optimized by numba, which is JIT compiler for numerical functions. The tokenizer is trained on a subset of the datasets for model pre-training.

Tech Blog

Bias, Risks, and Limitations

PLaMo 2 1B is a new technology that carries risks with use. Testing conducted to date has been in English and Japanese, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, PLaMo 2 1B’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of PLaMo 2 1B, developers should perform safety testing and tuning tailored to their specific applications of the model.

Acknowledgement

This model is trained under the project, “Research and Development Project of the Enhanced Infrastructures for Post 5G Information and Communication System” (JPNP 20017), subsidized by the New Energy and Industrial Technology Development Organization (NEDO).

pfnet
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plamo-2-1b