Instruction Synthesizer
| Property | Value |
|---|---|
| Parameter Count | 7.24B |
| License | Apache 2.0 |
| Paper | View Paper |
| Language | English |
| Framework | Text Generation Inference |
What is instruction-synthesizer?
The instruction-synthesizer is a specialized language model designed to automatically generate high-quality instruction-response pairs from raw text. It represents a breakthrough in instruction pre-training, enabling the scalable augmentation of massive raw corpora with synthetic instruction data. This model is part of research accepted at EMNLP 2024, demonstrating superior performance in both pre-training from scratch and domain-adaptive continual pre-training scenarios.
Implementation Details
Built on the Mistral architecture, this model leverages context-based instruction synthesis to generate meaningful instruction-response pairs. The model has been fine-tuned on a carefully curated dataset to ensure high-quality output generation.
- Efficient processing capable of synthesizing instruction-response pairs for 1 billion tokens of raw corpora in approximately 1 day on a single A100-80GB GPU
- Implements vLLM for accelerated inference
- Supports both basic usage for individual text processing and advanced scaling for large corpus augmentation
Core Capabilities
- Generation of contextually relevant instruction-response pairs from raw text
- Support for both single-text processing and batch processing of large corpora
- Templification of few-shot examples for pre-training
- Integration with popular training frameworks like OLMo and LLaMA-Factory
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely enables automated generation of instruction-response pairs from raw text, facilitating efficient instruction pre-training at scale. It's particularly notable for its ability to improve both pre-trained base models and their subsequent instruction tuning performance.
Q: What are the recommended use cases?
The model is ideal for researchers and developers working on language model pre-training, particularly those interested in domain adaptation or scaling instruction tuning. It's especially useful for converting domain-specific corpora into instruction-augmented training data.
