textsftreasoningchain-of-thoughtjapanesellm-training

LLM-jp-4 Thinking Japanese Reasoning SFT Dataset

Free

Open dataset

Sample structure: 100 / 100

2 download links issued

Seller: DataBazaar

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Source, license and coverage

Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.

License: Per-subset licenses (see source card)
Source / creator: huggingface: llm-jp/llm-jp-4-thinking-sft-data
Collection method: auto_imported_huggingface_federated
Coverage start: Not documented
Coverage end: Not documented
Data last updated: Not documented
Update schedule: Not documented

Source documentation ↗

License terms ↗

The source card lists separate terms for each included subset. Do not treat all subsets as CC BY.

Sample structure score: 100 / 100

This automated check describes the inspected sample, not factual accuracy, legal rights, representativeness, or the quality of the entire dataset. It is not a customer rating.

Assessed 4 sample records (JSON) on 2026-10-09. All records in the provided sample were checked.

Check	Points	Evidence
Populated cells	50 / 50	8 of 8 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated.
Consistent value types	30 / 30	8 of 8 populated cells match their column's most common observed type. Types are inferred, not checked against real-world truth.
Consistent record shape	20 / 20	4 of 4 records have the expected fields. CSV/TSV use the header width; JSON uses the union of observed keys.

Field-level findings and improvements

Check missing cells and mixed types below. Document intentional missing values or mixed types in your field descriptions. Do not fill legitimate unknowns with invented values just to increase this score.

Field	Missing cells	Most common type	Other populated types
ID	0 / 4	string	0 / 4
messages	0 / 4	string	0 / 4

How the score is calculated, its limitations, and how to correct an assessment →

About this data

Supervised fine-tuning dataset pairing Japanese prompts from diverse sources with step-by-step reasoning traces and final responses for chain-of-thought model training.

Retrieve with your agent or Python

Create an account and configure DATABAZAAR_API_KEY. This example retrieves free or already purchased data; it never makes a purchase. For a multi-file dataset, choose a file index from the manifest.

Download the Python example

python3 retrieve-dataset.py ca5322c9-179c-41e8-b3fe-a5ad10f214cc --output dataset.bin

Full supplier documentation

~3.2M supervised fine-tuning examples used to train the llm-jp-4 "thinking" models. Prompts drawn from diverse sources are paired with generated step-by-step reasoning traces and final responses for chain-of-thought training.

Schema

Name	Type	Description
ID	VARCHAR	Unique identifier for the training example, formatted as dataset_date_category_number_language
messages	VARCHAR	JSON array of conversation objects with role, name, and content fields in OpenAI message format

Sample Data

Preview a sample of the data before downloading.

Public sample only. Sign in to retrieve the full dataset, including free datasets.

For AI Agents

Via MCP Server

# 1. Add to your agent's MCP config (claude_desktop_config.json or similar):
{
  "mcpServers": {
    "databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
  }
}

# 2. Your agent can then call:
search_datasets({ query: "LLM-jp-4 Thinking Japanese Rea" })
// Found: ca5322c9-179c-41e8-b3fe-a5ad10f214cc
get_download_url({ dataset_id: "ca5322c9-179c-41e8-b3fe-a5ad10f214cc" })  // free — sign in with MCP OAuth first

Via REST API

# Free dataset — sign in or use your account API key:
curl https://api.databazaar.io/datasets/ca5322c9-179c-41e8-b3fe-a5ad10f214cc/download-url -H "Authorization: Bearer $DATABAZAAR_API_KEY"