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Qwen2.5 7B Instruct AWQ by Qwen

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Qwen2.5 7B Instruct AWQ is an open-source language model by Qwen. Features: 7b LLM, VRAM: 5.6GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.26.

  Arxiv:2309.00071   Arxiv:2407.10671   4-bit   Awq Base model:quantized:qwen/qwen... Base model:qwen/qwen2.5-7b-ins...   Chat   Conversational   En   Endpoints compatible   Instruct   Quantized   Qwen2   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-AWQ 

Qwen2.5 7B Instruct AWQ Benchmarks

LLME Score: 0.25972
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Qwen2.5 7B Instruct AWQ (Qwen/Qwen2.5-7B-Instruct-AWQ)
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Qwen2.5 7B Instruct AWQ Parameters and Internals

Model Type 
Causal Language Model
Use Cases 
Areas:
research, commercial applications
Supported Languages 
en (English), zh (Chinese), fr (French), es (Spanish), pt (Portuguese), de (German), it (Italian), ru (Russian), ja (Japanese), ko (Korean), vi (Vietnamese), th (Thai), ar (Arabic)
Training Details 
Methodology:
Pretraining & Post-training; transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
Context Length:
131072
Model Architecture:
transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
Input Output 
Accepted Modalities:
text
Performance Tips:
Add the `rope_scaling` configuration when handling long texts.
LLM NameQwen2.5 7B Instruct AWQ
Repository 🤗https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-AWQ 
Base Model(s)  Qwen/Qwen2.5-7B-Instruct   Qwen/Qwen2.5-7B-Instruct
Model Size7b
Required VRAM5.6 GB
Updated2026-04-13
MaintainerQwen
Model Typeqwen2
Instruction-BasedYes
Model Files  4.0 GB: 1-of-2   1.6 GB: 2-of-2
Supported Languagesen
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.41.1
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size152064
Torch Data Typefloat16
Errorsreplace

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Rank the Qwen2.5 7B Instruct AWQ 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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