Tulu V2 SFT Mixture
Conversational corpus optimized for supervised fine-tuning (SFT) of language models, based on a mixture of synthetic and human instructions.
Description
The dataset Tulu V2 SFT Mixture is a corpus of 326,154 examples designed to train language models to follow instructions. It brings together synthetic and human dialogues covering various tasks such as reasoning, summaries, explanations or open answers. Distributed in Parquet format, it is lightweight (555 MB) and ready to use for modern AI frameworks.
What is this dataset for?
- Supervised fine-tuning (SFT) of LLM models so that they follow instructions accurately
- Training chatbots or intelligent conversational assistants
- Benchmark or validate models on realistic text generation scenarios
Can it be enriched or improved?
Yes, users can refine or supplement this dataset with their own instruction data or translations. It can also be filtered to create thematic subsets, or enriched with metadata (difficulty, domain, etc.) for specific uses such as RLHF or multi-task learning.
🔎 In summary
🧠 Recommended for
- LLM developers
- NLP researchers
- AI startups
🔧 Compatible tools
- Hugging Face Transformers
- LoRa
- OpenChatKit
- PyTorch
💡 Tip
For even better results, combine Tulu with real or multi-lingual conversational data.
Frequently Asked Questions
What type of models can you train with this dataset?
It is ideal for LLM-type models such as LLama, Mistral, or GPT-like, in conditional generation or instruction-following tasks.
Is this data set multilingual?
No, it's mostly in English. For multilingual, it will be necessary to complete or translate it.
Do you need to clean first before use?
No, the data is already well structured. A light pretreatment is sufficient, depending on the needs of the model.
