What are the hardware requirements for Qwen2.5 7B Instruct AWQ?

Qwen2.5 7B Instruct AWQ requires approximately 5.6 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen2.5 7B Instruct AWQ and how large is it?

Qwen2.5 7B Instruct AWQ is developed by Qwen, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Qwen2.5 7B Instruct AWQ?

Qwen2.5 7B Instruct AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 7B Instruct AWQ by Qwen — VRAM 5.6GB, 32K context

Name: Qwen2.5 7B Instruct AWQ
Author: Qwen

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 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 Name	Qwen2.5 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 Size	7b
Required VRAM	5.6 GB
Updated	2026-07-29
Maintainer	Qwen
Model Type	qwen2
Instruction-Based	Yes
Model Files	4.0 GB: 1-of-2 1.6 GB: 2-of-2
Supported Languages	en
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.41.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	float16
Errors	replace

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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