datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Tachibana4-DeepSeek-V4-ProClick here to support our open-source dataset and model releases - help us speed up our release schedule!
Tachibana 4 is an agentic coding dataset, testing the limits of DeepSeek-V4-Pro's coding skills:
Questions prioritize real-world, challenging agentic coding tasks across a variety of programming languages and topics. Synthethic prompts utilize a variety of personas, experience levels, and styles of communication to maximize real-world flexibility and usability.
Areas of focus include… See the full description on the dataset page: https://huggingface.co/datasets/sequelbox/Tachibana4-DeepSeek-V4-Pro.TachibanaTachibana is a dataset containing code-instruct data.
The 2024-09-27 version contains:
104k rows of synthetic chat responses generated using Llama 3.1 405b Instruct.
60.6k Magicoder prompts from ise-uiuc/Magicoder-Evol-Instruct-110K
43.4k Glaive-code-assistant prompts from glaiveai/glaive-code-assistant
This dataset contains synthetically generated data and has not been subject to manual review.
deepseek-v4-pro-tachibana4Click here to support our open-source dataset and model releases - help us speed up our release schedule!
Tachibana 4 is an agentic coding dataset, testing the limits of DeepSeek-V4-Pro's coding skills:
Questions prioritize real-world, challenging agentic coding tasks across a variety of programming languages and topics. Synthethic prompts utilize a variety of personas, experience levels, and styles of communication to maximize real-world flexibility and usability.
Areas of focus include… See the full description on the dataset page: https://huggingface.co/datasets/ansulev/deepseek-v4-pro-tachibana4.Tachibana2-DeepSeek-R1Click here to support our open-source dataset and model releases!
Tachibana2-DeepSeek-R1 is a code-reasoning dataset, testing the limits of DeepSeek R1's coding skills!
This dataset contains:
27.2k synthetically generated code-reasoning prompts. All responses are generated using DeepSeek R1.
Synthetic prompts are generated using Llama 3.1 405b Instruct, based on the original sequelbox/Tachibana dataset with increased task complexity.
Responses demonstrate the code-reasoning capabilities of… See the full description on the dataset page: https://huggingface.co/datasets/sequelbox/Tachibana2-DeepSeek-R1.Tachibana4-DeepSeek-V4-Pro-PREVIEWClick here to support our open-source dataset and model releases - help us speed up our release schedule!
This is an early sneak preview of Tachibana 4, containing the first 1.2k rows!
Tachibana 4 is an upcoming agentic coding dataset, generated by DeepSeek-V4-Pro:
Questions prioritize real-world, challenging agentic coding tasks across a variety of programming languages and topics.
Areas of focus include back-end and front-end development, systems programming, distributed systems… See the full description on the dataset page: https://huggingface.co/datasets/sequelbox/Tachibana4-DeepSeek-V4-Pro-PREVIEW.Tachibana-QVQTachibana-QVQ is a dataset containing code-reasoning and code-instruct responses across a wide variety of programming tasks.
This dataset contains:
103k prompts from sequelbox/Tachibana, with all responses generated by Qwen/QVQ-72B-Preview.
Responses demonstrate QVQ's code-reasoning ability and general code capabilities.
Responses have not been filtered or edited at all: some responses will contain infinite thought loops, incomplete answers, inaccurate responses, or other identified or… See the full description on the dataset page: https://huggingface.co/datasets/sequelbox/Tachibana-QVQ.tachibana_coding_10kTachibana3-Part2-DeepSeek-V3.2Click here to support our open-source dataset and model releases!
Tachibana3-Part2-DeepSeek-V3.2 is a dataset focused on high-difficulty code production tasks, testing the limits of DeepSeek V3.2's code-reasoning skills!
This dataset contains 9.3k high-difficulty code-production prompts:
Questions prioritize real-world, challenging coding tasks across a variety of programming languages and topics.
Areas of focus include back-end and front-end development, mobile, gamedev, cloud, QA, custom… See the full description on the dataset page: https://huggingface.co/datasets/sequelbox/Tachibana3-Part2-DeepSeek-V3.2.Tachibana3-Part1-DeepSeek-V3.1-TerminusClick here to support our open-source dataset and model releases!
Tachibana3-Part1-DeepSeek-V3.1-Terminus is a dataset focused on high-difficulty code production tasks, testing the limits of DeepSeek V3.1 Terminus's code-reasoning skills!
This dataset contains 9.3k high-difficulty code-production prompts:
Questions prioritize real-world, challenging coding tasks across a variety of programming languages and topics.
Areas of focus include back-end and front-end development, mobile, gamedev… See the full description on the dataset page: https://huggingface.co/datasets/sequelbox/Tachibana3-Part1-DeepSeek-V3.1-Terminus.Tachibana-QVQ-PREVIEWThis is a preview of the full Tachibana-QVQ code-instruct dataset, containing the first ~10k rows.
Get the full dataset now!
Prompts randomly selected from sequelbox/Tachibana, all responses generated by Qwen/QVQ-72B-Preview.
Dataset has not been reviewed for format or accuracy. Synthetic data is generated by a 'preview' edition of Qwen's QVQ 72b model.
Use as you will.
Kanade_Tachibana_Videos_Captioned
sequelbox_Tachibana2-DeepSeek-R1-PREVIEW-Shuffled-ShareGPTimport json
from tqdm import tqdm
from datasets import load_dataset
import pandas as pd
# Example usage:
dataset = load_dataset("sequelbox/Tachibana2-DeepSeek-R1-PREVIEW")["train"]
dataset = dataset.shuffle(seed=42)
output_file = "./sequelbox_Tachibana2-DeepSeek-R1-PREVIEW-Shuffled-ShareGPT.parquet"
data = []
for item in tqdm(dataset):
if item["prompt"].strip() == "" or item["response"].strip() == "":
continue
data.append(
{
"conversations": [… See the full description on the dataset page: https://huggingface.co/datasets/PJMixers-Dev/sequelbox_Tachibana2-DeepSeek-R1-PREVIEW-Shuffled-ShareGPT.