Qwen 2.5 Requeriments

Explore the groundbreaking capabilities of Qwen 2.5 models, Alibaba’s latest innovation in artificial intelligence. From the versatile Qwen 2.5 to specialized variants in coding, mathematics, vision-language, and audio, these models offer exceptional performance across diverse tasks. With sizes ranging from 0.5B to 72B parameters, Qwen 2.5 models cater to various computational resources and application needs. Discover how these state-of-the-art models are pushing the boundaries of AI, from natural language processing to multimodal understanding.

Qwen 2.5 Requirements

Model Category Specification Details
Qwen 2.5-0.5B Model Specifications GPU Memory 398MB
Storage Space <1GB
Max Length 32K tokens
Pretrained Tokens 2.2T
Min GPU Memory (Q-LoRA Finetuning) 5.8GB
Min GPU Memory (Generating 2048 Tokens, Int4) 2.9GB
License Apache 2.0
Qwen 2.5-1.5B Model Specifications GPU Memory 986MB
Storage Space ~2GB
Max Length 32K tokens
Tool Usage Supported
License Apache 2.0
Qwen 2.5-3B Model Specifications GPU Memory 1.9GB
Storage Space ~4GB
Max Length 32K tokens (estimated)
Tool Usage Likely supported
License Qwen-specific license
Qwen 2.5-7B Model Specifications GPU Memory 4.7GB
Max Length 32K tokens
Pretrained Tokens 2.4T
Min GPU Memory (Q-LoRA Finetuning) 11.5GB
Min GPU Memory (Generating 2048 Tokens, Int4) 8.2GB
Tool Usage Supported
License Apache 2.0
Qwen 2.5-14B Model Specifications GPU Memory 9.0GB
Max Length 32K tokens
Pretrained Tokens 3.0T
Min GPU Memory (Q-LoRA Finetuning) 18.7GB
Min GPU Memory (Generating 2048 Tokens, Int4) 13.0GB
Tool Usage Supported
License Apache 2.0
Qwen 2.5-32B Model Specifications GPU Memory 20GB
Max Length 32K tokens (estimated)
Pretrained Tokens Likely 3.0T or more
Tool Usage Likely supported
License Apache 2.0
Qwen 2.5-72B Model Specifications GPU Memory (BF16) 134.74GB (2 GPUs)
GPU Memory (GPTQ-Int8) 71.00GB (2 GPUs)
GPU Memory (GPTQ-Int4) 41.80GB (1 GPU)
GPU Memory (AWQ) 41.31GB (1 GPU)
Max Length 32K tokens
Pretrained Tokens 3.0T
Min GPU Memory (Q-LoRA Finetuning) 61.4GB
Min GPU Memory (Generating 2048 Tokens, Int4) 48.9GB
Tool Usage Supported

Qwen 2.5 Coder Requirements

Model Category Specification Details
Qwen 2.5 Coder 1.5B Technical Specifications Model Size 1.5 billion parameters
GPU Memory Approximately 986MB
Storage Space ~2GB
Max Length 32K tokens (estimated)
Pretrained Tokens Not specified, likely around 2.2T tokens
Key Features Optimized Architecture Designed specifically for coding tasks, offering a good balance between performance and resource efficiency
Processing Efficiency Capable of handling coding tasks with moderate computational resources
Advanced Technologies Incorporates technologies like flash-attention for improved efficiency and reduced memory usage
Linguistic Versatility Optimized for coding but maintains general natural language processing capabilities
System Requirements Python 3.8 or higher
PyTorch 1.12 or higher, 2.0+ recommended
CUDA 11.4 or higher (for GPU users)
Ideal Applications Coding assistance for small to medium-scale projects
Code generation and basic debugging
Ideal for individual developers or small teams with limited computational resources
Suitable for developers seeking assistance without high-end hardware
Qwen 2.5 Coder 7B Technical Specifications Model Size 7 billion parameters
GPU Memory 4.7GB
Max Length 32K tokens
Pretrained Tokens 2.4T
Min GPU Memory (Q-LoRA Finetuning) 11.5GB
Min GPU Memory (Generating 2048 Tokens, Int4) 8.2GB
Performance Characteristics Generation Speed (BF16) 37.97 tokens/s (input length 1)
Generation Speed (GPTQ-Int4) 36.17 tokens/s (input length 1)
Generation Speed (AWQ) 33.08 tokens/s (input length 1)
GPU Memory Usage (BF16) 14.92GB (input length 1)
GPU Memory Usage (GPTQ-Int4) 6.06GB (input length 1)
GPU Memory Usage (AWQ) 5.93GB (input length 1)
Key Features Advanced Coding Capabilities Significantly improved performance in complex coding tasks compared to the 1.5B model
Enhanced Contextual Understanding Better comprehension of context and developer intent due to larger parameter count
Support for Larger Projects Capable of handling more extensive and complex codebases
Programming Language Versatility Likely offers support for a wider range of programming languages and frameworks

Qwen 2 Math Requirements

Model Category Specification Details
Qwen2-Math 1.5B Technical Specifications Model Size 1.5 billion parameters
Non-Embedded Parameters 1.2B
GSM8K Performance 58.5%
MATH Performance 21.7%
MMLU Performance 56.5%
C-Eval Performance 70.6%
CMMLU Performance 70.3%
Additional Features Architecture Based on Transformer with improvements like SwiGLU activation
Tokenizer Improved and adaptive for multiple natural languages and code
Maximum Context 32K tokens (estimated, based on other Qwen2 models)
Qwen2-Math 7B Technical Specifications Model Size 7 billion parameters
GSM8K Performance 89.9%
MATH Improvement 5.0 points over its predecessor
Maximum Context 32K tokens
Quantization Options Available in BF16, GPTQ-Int8, GPTQ-Int4, and AWQ versions
Generation Speed BF16 37.97 tokens/s (input length 1)
GPTQ-Int4 36.17 tokens/s (input length 1)
AWQ 33.08 tokens/s (input length 1)
GPU Memory Usage BF16 14.92GB (input length 1)
GPTQ-Int4 6.06GB (input length 1)
AWQ 5.93GB (input length 1)
Qwen2-Math 72B Technical Specifications Model Size 72 billion parameters
MATH Benchmark 84%
GSM8K Performance 96.7%
College Math Performance 47.8%
MMLU Performance 84.2%
GPQA Performance 37.9%
HumanEval Performance 64.6%
BBH Performance 82.4%
Additional Features Maximum Context 128K tokens
License Qwen-specific (not Apache 2.0 like smaller models)
System Requirements (estimated) GPU Memory (BF16) ~134GB (2 GPUs)
GPU Memory (GPTQ-Int8) ~71GB (2 GPUs)
GPU Memory (GPTQ-Int4) ~42GB (1 GPU)
GPU Memory (AWQ) ~41GB (1 GPU)

Qwen 2 Vl Requirements

Model Category Specification Details
Qwen2-VL-2B Model Composition Total Size 2 billion parameters
Vision Encoder 675M parameters
LLM 1.5B parameters
Hardware Requirements GPU CUDA compatible, minimum 4GB VRAM
CPU 4 cores or more
RAM 8GB minimum, 16GB recommended
Software Requirements Python 3.8 or higher
PyTorch 1.12 or higher
Transformers 4.32.0 or higher
Storage Disk Space Approximately 4GB
Performance MMMU val 41.1%
DocVQA test 90.0%
Processing Capabilities Images Up to 2048×2048 pixels
Video Up to 20 minutes duration
License Apache 2.0
Qwen2-VL-7B Model Composition Total Size 7 billion parameters
Vision Encoder 675M parameters
LLM 7.6B parameters
Hardware Requirements GPU CUDA compatible, minimum 16GB VRAM
CPU 8 cores or more
RAM 32GB minimum, 64GB recommended
Software Requirements Python 3.8 or higher
PyTorch 2.0 or higher
Transformers 4.37.0 or higher
Storage Disk Space Approximately 14GB
Performance Outperforms OpenAI GPT-4o mini in most benchmarks
Processing Capabilities Images Dynamic resolution up to 4096×4096 pixels
Video Up to 20 minutes duration, processing 2 frames per second
License Apache 2.0
Qwen2-VL-72B Model Composition Total Size 72 billion parameters
Vision Encoder 675M parameters
LLM 72B parameters
Hardware Requirements GPU Multiple high-end GPUs, minimum 2x NVIDIA A100 80GB
CPU 32 cores or more
RAM 256GB minimum, 512GB recommended
Software Requirements Python 3.8 or higher
PyTorch 2.0 or higher
Transformers 4.37.0 or higher
Storage Disk Space More than 130GB
Performance State-of-the-art in MathVista, DocVQA, RealWorldQA, and MTVQA
Processing Capabilities Images Dynamic resolution with no theoretical limit
Video More than 20 minutes duration, with advanced frame processing
Access Available through official API

Qwen 2 Audio Requirements

Category Specification Details
Model Composition Total Size 7 billion parameters
Vision Encoder 675M parameters
LLM 7.6B parameters
Hardware Requirements GPU CUDA compatible, minimum 16GB VRAM recommended
CPU 8 cores or more for optimal performance
RAM 32GB minimum, 64GB or more recommended
Storage At least 20GB free disk space for the model and dependencies
Software Requirements Operating System Linux (Ubuntu 20.04 or higher recommended), Windows 10/11 with WSL2, or macOS 11 or higher
Python 3.8 or higher
PyTorch 2.0 or higher, compiled with CUDA support
Transformers 4.37.0 or higher, recommended to install the latest version from GitHub:
pip install git+https://github.com/huggingface/transformers
Librosa Latest stable version for audio processing
FFmpeg Required for audio file manipulation
Additional Dependencies
  • CUDA Toolkit: Version 11.4 or higher
  • cuDNN: Version compatible with installed CUDA version
  • Numpy: Latest stable version
  • SoundFile: For reading and writing audio files
  • Torchaudio: For audio processing in PyTorch
Network Requirements Internet Connection Stable connection for model download (approximately 14GB)
Recommended Bandwidth 100 Mbps or higher for fast download
Processing Capabilities Images Dynamic resolution up to 4096×4096 pixels
Video Up to 20 minutes duration, processing 2 frames per second
Performance Outperforms OpenAI GPT-4o mini in most benchmarks
License Apache 2.0

Frequently Asked Questions (FAQ)

1. What are the main differences between Qwen 2.5 model sizes?

Model Size Differences

Qwen 2.5 models range from 0.5B to 72B parameters. Larger models like 72B offer superior performance and capabilities but require more computational resources, while smaller models like 0.5B are more suitable for limited hardware setups.

2. Can I run Qwen 2.5 models on my personal computer?

Running on Personal Computers

It depends on the model size and your hardware. Smaller models like Qwen 2.5-0.5B can run on consumer-grade hardware with 4GB VRAM, while larger models like Qwen 2.5-72B require multiple high-end GPUs and are better suited for server environments.

3. What are the key features of Qwen 2.5 Coder models?

Qwen 2.5 Coder Features

Qwen 2.5 Coder models are optimized for programming tasks, offering improved code generation and understanding. They feature advanced technologies like flash-attention for better efficiency and can handle complex coding tasks with moderate computational resources.

4. How do Qwen 2 Math models perform in mathematical tasks?

Qwen 2 Math Performance

Qwen 2 Math models show impressive performance on various math benchmarks. For instance, the 72B model achieves 84% on the MATH benchmark and 96.7% on GSM8K, demonstrating strong capabilities in mathematical reasoning and problem-solving.

5. What are the image processing capabilities of Qwen 2 VL models?

Qwen 2 VL Image Processing

Qwen 2 VL models can process images with varying resolutions. The 2B model handles up to 2048×2048 pixels, the 7B model up to 4096×4096 pixels, and the 72B model has no theoretical resolution limit, offering dynamic resolution processing.

6. Are there any licensing restrictions for using Qwen models?

Licensing Information

Most Qwen models, including smaller versions, are available under the Apache 2.0 license. However, some larger models like Qwen 2-Math 72B have a Qwen-specific license. Always check the official documentation for the most up-to-date licensing information.

7. What software requirements are needed to run Qwen 2 Audio models?

Qwen 2 Audio Software Requirements

Qwen 2 Audio models require Python 3.8 or higher, PyTorch 2.0 or higher with CUDA support, and specific libraries like Librosa and FFmpeg. Additional dependencies include CUDA Toolkit 11.4+, cuDNN, Numpy, SoundFile, and Torchaudio.

8. How do different quantization options affect Qwen model performance?

Quantization Effects on Performance

Quantization options like BF16, GPTQ-Int8, GPTQ-Int4, and AWQ affect both performance and memory usage. For example, in the 7B model, BF16 offers the highest performance but uses more GPU memory (14.92GB), while GPTQ-Int4 reduces memory usage to 6.06GB with a slight decrease in generation speed.

Qwen 2.5 models showcase impressive advancements in AI, offering versatile solutions from 0.5B to 72B parameters. With specialized variants for coding, math, vision-language, and audio tasks, they excel in diverse applications. These models represent the cutting edge of AI technology, empowering developers to tackle complex challenges across multiple domains.