Qwen 2.5 7B is a decoder-only large language model developed by the Qwen team at Alibaba Cloud. It belongs to the Qwen 2.5 family of models, which ranges from 0.5B to 72B parameters. The 7B model specifically contains 6.53B parameters (excluding embeddings) and employs a transformer architecture incorporating RoPE, SwiGLU, RMSNorm, and Attention QKV bias. The architecture features 28 layers and 28 attention heads for Q with 4 for KV.
The model was trained on a massive dataset of up to 18 trillion tokens, resulting in significant knowledge gains compared to its predecessor, Qwen 2. This extensive training has enabled the model to handle a context length of up to 131,072 tokens, making it particularly well-suited for processing and generating long-form content.
Capabilities and Features
Qwen 2.5 7B demonstrates several key improvements over previous versions. The model excels at generating longer texts (over 8K tokens) and shows enhanced capabilities in understanding and generating structured data, particularly JSON and tables. It exhibits improved instruction following abilities and increased resilience to diverse system prompts, making it more reliable for various applications.
The model supports multilingual capabilities across more than 29 languages, though specific language performance metrics are not detailed in the documentation. Notable strengths include:
- Enhanced coding and mathematics capabilities due to specialized training
- Improved structured data handling and generation
- Better instruction following compared to previous versions
- Long-context processing (up to 128K tokens)
- Output generation up to 8K tokens
It's important to note that the developers recommend against using the base model directly for conversations. Instead, they suggest applying post-training techniques such as SFT, RLHF, or continued pretraining for optimal performance in conversational applications.
Technical Implementation and Usage
The model requires the Hugging Face transformers library (version 4.37.0 or later) for implementation. It can be deployed through several frameworks and environments, including:
- Hugging Face Transformers
- vLLM (with tool calling support via vllm>=0.6)
- Ollama (with tool calling support)
- llama.cpp for local execution
The model is licensed under Apache 2.0, making it freely available for both research and commercial applications. This is consistent across the Qwen 2.5 family, except for the 3B and 72B variants which have different licensing terms.
Model Family and Comparisons
Within the Qwen 2.5 family, several specialized variants complement the base 7B model:
- Qwen2.5-Coder: Trained on 5.5 trillion tokens of code-related data
- Qwen2.5-Math: Supports both Chinese and English, incorporating Chain-of-Thought (CoT), Program-of-Thought (PoT), and Tool-Integrated Reasoning (TIR) methods
The 7B model shows competitive performance against other open-source models of similar or larger size in benchmarks like MMLU, HumanEval, and MATH, demonstrating strong capabilities in reasoning and problem-solving tasks.
References and Additional Resources
