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Vikhr 7B Instruct 0.4 by Vikhrmodels

 »  All LLMs  »  Vikhrmodels  »  Vikhr 7B Instruct 0.4   URL Share it on

  6001   
  Arxiv:2405.13929   Autotrain compatible   Conversational   En   Endpoints compatible   Instruct   Llama   Region:us   Ru   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Vikhrmodels/Vikhr-7B-instruct_0.4 

Vikhr 7B Instruct 0.4 Benchmarks

LLME Score: 0.22166
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Vikhr 7B Instruct 0.4 (Vikhrmodels/Vikhr-7B-instruct_0.4)

Vikhr 7B Instruct 0.4 Parameters and Internals

Model Type 
Instruction-Tuned, Causal Language Model
Use Cases 
Areas:
Research, Commercial Applications
Applications:
Text Generation
Primary Use Cases:
Response to prompts in Russian and English
Supported Languages 
ru (high), en (high)
Training Details 
Data Sources:
SFT
Methodology:
Instruction-Tuned
Model Architecture:
Flash Attention 2
Input Output 
Input Format:
Prompts using chat template
Accepted Modalities:
text
Output Format:
Textual response
Release Notes 
Version:
0.4
Date:
2023-10-04
Notes:
Added a lot more data to SFT, now JSON and multiturn work more stable on long context and hard prompts
LLM NameVikhr 7B Instruct 0.4
Repository 🤗https://huggingface.co/Vikhrmodels/Vikhr-7B-instruct_0.4 
Model Size7b
Required VRAM15.2 GB
Updated2025-02-22
MaintainerVikhrmodels
Model Typellama
Instruction-BasedYes
Model Files  4.9 GB: 1-of-4   5.0 GB: 2-of-4   4.7 GB: 3-of-4   0.6 GB: 4-of-4
Supported Languagesru en
Model ArchitectureMistralForCausalLM
Context Length32768
Model Max Length32768
Transformers Version4.38.1
Tokenizer ClassLlamaTokenizer
Padding Token</s>
Vocabulary Size79099
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than Vikhrmodels/Vikhr-7B-instruct_0.4.

Rank the Vikhr 7B Instruct 0.4 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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