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Ninja V2 7B by Local-Novel-LLM-project

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Ninja V2 7B is an open-source language model by Local-Novel-LLM-project. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, Quantized, LLM Explorer Score: 0.14.

  Endpoints compatible   Mistral   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF ๐Ÿค—: https://huggingface.co/Local-Novel-LLM-project/Ninja-V2-7B 

Ninja V2 7B Benchmarks

LLME Score: 0.14079
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Ninja V2 7B (Local-Novel-LLM-project/Ninja-V2-7B)
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Ninja V2 7B Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
general-purpose including chat and text creation
Applications:
Wide range of functions including chat and writing tasks
Primary Use Cases:
Text generation, chat applications
Training Details 
Methodology:
Creating combination models using a 'mergekit' and 'ninja_merger' with methods like 'sub','mix','add'. Consists of combining multiple pre-trained models.
Hardware Used:
High performance GPU server from LocalAI ใƒใƒƒใ‚ซใ‚ฝใƒณ
Input Output 
Input Format:
Vicuna-1.1 template can be used but is not mandatory
Performance Tips:
No preferred template for simple text generation.
LLM NameNinja V2 7B
Repository ๐Ÿค—https://huggingface.co/Local-Novel-LLM-project/Ninja-V2-7B 
Base Model(s)  Nina V2 7B   Virt-io/Nina-v2-7B
Model Size7b
Required VRAM14.4 GB
Updated2026-04-08
MaintainerLocal-Novel-LLM-project
Model Typemistral
Model Files  4.9 GB: 1-of-3   5.0 GB: 2-of-3   4.5 GB: 3-of-3
Quantization Typeq8|gguf
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.41.1
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32000
Torch Data Typebfloat16

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Astral 256K 7B V2250K / 14.4 GB50
Astral 256K 7B250K / 14.4 GB50
Note: green Score (e.g. "73.2") means that the model is better than Local-Novel-LLM-project/Ninja-V2-7B.

Rank the Ninja V2 7B Capabilities

๐Ÿ†˜ Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! ๐ŸŒŸ

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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