Vikhr-Nemo-12B-Instruct-R-21-09-24
| Property | Value |
|---|---|
| Parameter Count | 12.2B |
| Model Type | Instruction-following LLM |
| Architecture | Mistral-based |
| License | Apache 2.0 |
| Paper | Research Paper |
What is Vikhr-Nemo-12B-Instruct-R-21-09-24?
Vikhr-Nemo is a flagship unimodal LLM developed by VikhrModels team, representing an enhanced version of the Mistral-Nemo-Instruct-2407 model. This bilingual powerhouse is specifically optimized for Russian and English language processing, incorporating advanced training techniques including SFT and SMPO (a proprietary variation of DPO).
Implementation Details
The model leverages a sophisticated training pipeline that includes multiple stages of refinement and optimization. It supports up to 128k tokens of context and implements a specialized Grounded RAG mode for enhanced document retrieval and question answering capabilities.
- Trained on 150k high-quality instruction dataset (GrandMaster-PRO-MAX)
- Incorporates built-in Chain-of-Thought reasoning
- Optimized through custom SMPO alignment process
- Supports multiple document formats including Markdown, HTML, and Plain Text
Core Capabilities
- Bilingual excellence in Russian and English
- Advanced reasoning and summarization
- Code generation capabilities
- Role-playing and dialogue maintenance
- High-performance RAG capabilities
- System prompt support for response style regulation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional bilingual capabilities and advanced RAG implementation, achieving performance metrics that compete with GPT-4o-mini in certain tasks. It features a unique SMPO alignment method and comprehensive training on diverse, high-quality datasets.
Q: What are the recommended use cases?
The model excels in reasoning tasks, summarization, code generation, roleplay scenarios, and maintaining coherent dialogues. It's particularly well-suited for RAG applications and can handle both domain-specific and general knowledge queries with high accuracy.
