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Vikhr Qwen 2.5 0.5B Instruct by Vikhrmodels

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Vikhr Qwen 2.5 0.5B Instruct is an open-source language model by Vikhrmodels. Features: 0.5b LLM, VRAM: 1GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.17.

  Arxiv:2405.13929 Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-0.5b-i...   Conversational Dataset:vikhrmodels/grandmaste...   Deploy:azure   En   Endpoints compatible   Instruct   Qwen2   Region:us   Ru   Safetensors
Model Card on HF 🤗: https://huggingface.co/Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct 

Vikhr Qwen 2.5 0.5B Instruct Benchmarks

LLME Score: 0.16637
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Vikhr Qwen 2.5 0.5B Instruct (Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct)
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Vikhr Qwen 2.5 0.5B Instruct Parameters and Internals

Model Type 
language model
Use Cases 
Areas:
research, commercial applications
Additional Notes 
The model is specifically designed for processing the Russian language and is efficient on low-end mobile devices.
Supported Languages 
en (high), ru (high)
Training Details 
Data Sources:
Vikhrmodels/GrandMaster-PRO-MAX
Data Volume:
150k instructions
Methodology:
Supervised Fine-Tuning (SFT) with Chain-Of-Thought prompts for GPT-4-turbo
Input Output 
Accepted Modalities:
text
Performance Tips:
Recommended generation temperature: 0.3
LLM NameVikhr Qwen 2.5 0.5B Instruct
Repository 🤗https://huggingface.co/Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct 
Model NameVikhr-Qwen-2.5-0.5b-Instruct
Base Model(s)  Qwen/Qwen2.5-0.5B-Instruct   Qwen/Qwen2.5-0.5B-Instruct
Model Size0.5b
Required VRAM1 GB
Updated2026-05-04
MaintainerVikhrmodels
Model Typeqwen2
Instruction-BasedYes
Model Files  1.0 GB
Supported Languagesru en
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.45.1
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151665
Torch Data Typefloat16
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than Vikhrmodels/Vikhr-Qwen-2.5-0.5b-Instruct.

Rank the Vikhr Qwen 2.5 0.5B Instruct Capabilities

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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