HQIT — A massive set of tuning instructions from LLM
Very large instruction-based dataset designed for fine-tuning LLM models via pairs of high-quality instructions and responses.
Description
HQIT is a massive dataset composed of nearly 2 million instruction-response pairs. It was designed for tuning language models (LLMs) to teach them how to better follow instructions. Each line contains a textual task formulated in the form of an instruction, as well as an associated response, simulating a natural interaction between a user and an AI.
What is this dataset for?
- Train or refine LLM models to be more aligned with human instructions
- Improving the quality of conversational assistants in various contexts (customer support, educational tools, etc.)
- Testing the robustness of LLMs on millions of varied and realistic cases
Can it be enriched or improved?
Yes. HQIT can be enriched by translations, additional annotations (difficulty, task category), or thematic selection. It can also be used as a basis for creating multilingual or multi-domain datasets.
🔎 In summary
🧠 Recommended for
- LLM developers
- NLP researchers
- AI startups
🔧 Compatible tools
- Hugging Face Transformers
- LoRa
- DeepSpeed
- LangChain
💡 Tip
Filter the longest pairs to pre-train more effectively on lightweight infrastructures.
Frequently Asked Questions
Can this dataset be used to train a personalized chat assistant?
Yes, HQIT provides an ideal basis for training an assistant via tuning instruction on millions of realistic cases.
Is the data multilingual?
The dataset seems mostly in English. It is possible to translate it to create a suitable multilingual version.
Is it suitable for training on limited GPUs?
Yes, by applying sampling or techniques like LoRa, it can be exploited on limited resources.
