ShadowFall09/FANNO
TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:4kPublished:Mar 22, 2024License:apache-2.0Architecture:Transformer Open Weights Cold
ShadowFall09/FANNO is a 7 billion parameter instruction-finetuned language model based on Llama2-7b, designed as an autonomous framework for generating high-quality instruction datasets. It excels at streamlining the annotation process for instruction datasets without requiring pre-existing annotated data, leveraging the Mistral-7b-instruct model for efficient data generation.
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Model Overview
ShadowFall09/FANNO is an innovative, fully autonomous, open-sourced framework built upon the Llama2-7b architecture, specifically instruction-finetuned for data annotation. This 7 billion parameter model is designed to streamline the annotation process for instruction datasets, eliminating the need for pre-existing annotated data.
Key Capabilities
- Autonomous Annotation: FANNO removes the dependency on manual annotations or costly API calls to proprietary LLMs, offering a cost-effective and efficient annotation solution.
- High-Quality Data Generation: The framework is capable of producing diverse and complex datasets that are comparable in quality to human-annotated or meticulously cleaned datasets, such as Alpaca-GPT4-Cleaned.
- Open-Sourced Framework: Being fully open-sourced, FANNO encourages community leverage and contributions, fostering continuous improvement and development.
Good For
- Instruction Dataset Creation: Ideal for generating high-quality instruction datasets autonomously.
- Cost-Effective Annotation: Suitable for projects aiming to reduce annotation costs and improve efficiency.
- Research and Development: Provides a flexible and open platform for researchers and developers to experiment with and enhance data annotation processes.