Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct
TEXT GENERATIONConcurrency Cost:1Model Size:0.5BQuant:BF16Ctx Length:32kPublished:Oct 5, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Warm
Vikhr-Qwen-2.5-0.5b-Instruct is a compact 0.5 billion parameter instruction-tuned language model developed by Vikhrmodels, based on Qwen-2.5-0.5B-Instruct. It is specifically fine-tuned on the Russian-language GrandMaster-PRO-MAX dataset, offering 4 times higher efficiency than its base model. This model is optimized for Russian language processing and designed for efficient deployment on low-end mobile devices.
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Vikhr-Qwen-2.5-0.5b-Instruct Overview
Vikhr-Qwen-2.5-0.5b-Instruct is a compact 0.5 billion parameter instruction-tuned language model developed by Vikhrmodels. It is built upon the Qwen-2.5-0.5B-Instruct architecture and has been specifically fine-tuned for the Russian language.
Key Capabilities & Features
- Russian Language Specialization: The model is trained on the
GrandMaster-PRO-MAXdataset, making it highly proficient in processing and generating Russian text. - High Efficiency: It boasts 4 times higher efficiency compared to its base model, making it suitable for resource-constrained environments.
- Compact Size: With a size of 1GB, it is ideal for deployment on low-end mobile devices.
- Supervised Fine-Tuning (SFT): The model was trained using SFT on a synthetic dataset of 150k instructions, incorporating Chain-Of-Thought (CoT) prompts derived from GPT-4-turbo.
Ideal Use Cases
- Mobile Device Deployment: Its small footprint and high efficiency make it perfect for applications running on mobile or other low-power devices.
- Russian Language Applications: Excellent for tasks requiring strong performance in Russian, such as chatbots, content generation, or summarization in Russian.
- Instruction Following: Designed to follow instructions effectively due to its instruction-tuned nature and CoT training.