Datasets:
language:
- ja
pretty_name: lambda-chat
license: other
license_name: mixed-source-licenses
license_link: https://huggingface.co/datasets/KeisukeMiyamoto/lambda-chat#source-data
license_details: Each row remains subject to the license and terms of its source dataset.
task_categories:
- text-generation
annotations_creators:
- no-annotation
language_creators:
- found
- machine-generated
multilinguality:
- monolingual
source_datasets:
- APTO-001/ja-safety-sft-dataset
- APTO-001/japanese-civil-law-llm-instruction-dataset
- APTO-001/instruction-following-dataset
- APTO-001/japanese-reasoning-dataset-sample
- APTO-001/japanese-style-contrast-dataset
- APTO-001/llm-safety-japanese-multiturn-dataset
- megagonlabs/instruction_ja
- llm-jp/extraction-wiki-ja
- llm-jp/llm-jp-instructions
- llm-jp/magpie-sft-v1.0
- OpenAssistant/oasst1
models:
- google/gemma-4-12B
tags:
- japanese
- instruction-following
- conversational
- parquet
size_categories:
- 100K<n<1M
configs:
- config_name: default
data_files:
- split: train
path: train/*.parquet
- split: validation
path: validation.parquet
- split: test
path: test.parquet
lambda-chat
lambda-chat is a Japanese instruction-following dataset for supervised fine-tuning of chat models. It combines openly available datasets into one consistent chat format for easier use.
Purpose
The dataset is intended for training and evaluating Japanese chat and instruction-following models. Each example uses a list of messages with role and content fields.
Source Data
The dataset contains data from the following sources.
- APTO-001/ja-safety-sft-dataset
- APTO-001/japanese-civil-law-llm-instruction-dataset
- APTO-001/instruction-following-dataset
- APTO-001/japanese-reasoning-dataset-sample
- APTO-001/japanese-style-contrast-dataset
- APTO-001/llm-safety-japanese-multiturn-dataset
- megagonlabs/instruction_ja
- llm-jp/extraction-wiki-ja
- llm-jp/llm-jp-instructions
- llm-jp/magpie-sft-v1.0
- OpenAssistant/oasst1
llm-jp/llm-jp-instructions and APTO-001/ja-safety-sft-dataset are published under CC BY 4.0 license.
Data Processing
The source data was deduplicated, normalized with NFKC, and filtered to remove code and LaTeX content. The examples were then randomly shuffled and split into train, validation, and test sets.
Dataset Size
| Split | Rows |
|---|---|
| train | 183,072 |
| validation | 1,868 |
| test | 1,868 |
| total | 186,808 |
The dataset contains about 50M assistant tokens measured by Gemma 4 12B tokenizer.
Columns
| Column | Description |
|---|---|
id |
Unique identifier for the example. |
messages |
Chat messages. Each message has role and content. |
output_char |
Number of characters in all assistant responses. |
output_token |
Number of tokens in all assistant responses. |
dataset_name |
Source dataset name |
Example
from datasets import load_dataset
dataset = load_dataset("KeisukeMiyamoto/lambda-chat")
print(dataset["train"][0])
Support Lambda
Lambda is an open-source project for building small Japanese language models from scratch. As a student, I have funded this project with income from my part-time job, but the growing training costs are becoming difficult to cover.
Your support helps cover GPU costs and develop larger models. Thank you for helping Lambda continue to grow.
Vast.ai
Vast.ai offers affordable cloud GPUs for AI training, with NVIDIA H100 SXM GPUs available from around $1.54 per hour. If you purchase credits through the link below, I receive 3% in GPU credits at no extra cost to you.
https://cloud.vast.ai/?ref_id=521936
Ko-fi
Support Lambda with a donation starting from $5.