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Qwen2 7B Instruct GGUF by gaianet

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Qwen2 7B Instruct GGUF is an open-source language model by gaianet. Features: 7b LLM, VRAM: 3GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.14.

Base model:quantized:qwen/qwen... Base model:qwen/qwen2-7b-instr...   Chat   Conversational   En   Endpoints compatible   Gguf   Instruct   Q2   Quantized   Qwen2   Region:us
Model Card on HF 🤗: https://huggingface.co/gaianet/Qwen2-7B-Instruct-GGUF 

Qwen2 7B Instruct GGUF Benchmarks

LLME Score: 0.13816
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Qwen2 7B Instruct GGUF (gaianet/Qwen2-7B-Instruct-GGUF)
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Qwen2 7B Instruct GGUF Parameters and Internals

Model Type 
text-generation
Input Output 
Input Format:
chatml
LLM NameQwen2 7B Instruct GGUF
Repository 🤗https://huggingface.co/gaianet/Qwen2-7B-Instruct-GGUF 
Model NameQwen2-7B-Instruct
Model CreatorQwen
Base Model(s)  Qwen2 7B Instruct   Qwen/Qwen2-7B-Instruct
Model Size7b
Required VRAM3 GB
Updated2026-04-22
Maintainergaianet
Model Typeqwen2
Instruction-BasedYes
Model Files  3.0 GB   4.1 GB   3.8 GB   3.5 GB   4.4 GB   4.7 GB   4.5 GB   5.3 GB   5.4 GB   5.3 GB   6.2 GB   8.1 GB   15.2 GB
Supported Languagesen
GGUF QuantizationYes
Quantization Typegguf|q2|q4_k|q5_k
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.41.2
Vocabulary Size152064
Torch Data Typebfloat16

Best Alternatives to Qwen2 7B Instruct GGUF

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...der 7B Instruct Abliterated V132K / 15.2 GB222
Qwen2 7B Instruct GGUF32K / 3 GB1061
Qwen2.5 7B Instruct 1M 4bit986K / 4.3 GB42210
...B Instruct 1M Unsloth Bnb 4bit986K / 7.5 GB773
Qwen2.5 7B Instruct 1M 8bit986K / 8.1 GB774
Qwen2.5 7B Instruct 1M 6bit986K / 6.2 GB302
Alisia 7B Instruct V1128K / 15.4 GB1142
Note: green Score (e.g. "73.2") means that the model is better than gaianet/Qwen2-7B-Instruct-GGUF.

Rank the Qwen2 7B Instruct GGUF 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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