Qwen2.5-7B
Parameters
7B
Context Length
131.072K
Modality
Text
Architecture
Dense
License
Apache 2.0
Release Date
19 Sept 2024
Knowledge Cutoff
-
Technical Specifications
Attention
Attention Structure
Grouped-Query Attention
Attention Heads
64
Key-Value Heads
8
Attention Head Dimension
-
Position Embedding
ROPE
RoPE Theta
1,000,000
Sliding Window Attention
No
Sliding Window Size
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
4,096
Number of Layers
32
FFN Intermediate Size (Dense)
18,944
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
152,064
Architecture Diagram
Qwen2.5-7B
Qwen2.5-7B is a foundational large language model developed by Alibaba Cloud, forming a part of the Qwen2.5 series. This model is a causal language model engineered for general-purpose applications, serving as a robust base for subsequent fine-tuning and specialized tasks. It is designed to extend the linguistic capabilities of its predecessors by incorporating an expanded knowledge base and enhancing performance in core language understanding and generation tasks. The model provides multilingual support, enabling processing across more than 29 languages. This versatility positions Qwen2.5-7B as a foundational component for diverse natural language processing systems.
Architecturally, Qwen2.5-7B employs a transformer-based encoder-decoder framework. Key architectural components include the integration of Rotary Position Embeddings (RoPE) for effective handling of sequence length and position, SwiGLU as its activation function for non-linearity, and RMSNorm for stable normalization across layers. The attention mechanism features Grouped Query Attention (GQA), optimizing computational efficiency by sharing key and value projections across multiple query heads. Specifically, the 7B variant utilizes 28 attention heads for queries and 4 for key/value pairs, distributed across 28 layers. This configuration facilitates efficient processing of long sequences.
The Qwen2.5-7B model is suitable for pretraining, providing a base for developers to build upon through further training stages such as Supervised Fine-Tuning (SFT) or Reinforcement Learning from Human Feedback (RLHF). While it is a base model, the Qwen2.5 family exhibits enhanced capabilities in areas such as coding and mathematics, benefiting from specialized expert models. It also demonstrates improved proficiency in instruction following, processing structured data, and generating extended text outputs, including formatted data like JSON. The model's capacity to handle context lengths up to 131,072 tokens supports the processing of substantially long inputs.
About Qwen2.5
Qwen2.5 by Alibaba is a family of dense, decoder-only language models available in various sizes, with some variants utilizing Mixture-of-Experts. These models are pretrained on large-scale datasets, supporting extended context lengths and multilingual communication. The family includes specialized models for coding, mathematics, and multimodal tasks, such as vision and audio processing.
Other Qwen2.5 Models
Evaluation Benchmarks
No evaluation benchmarks for Qwen2.5-7B available.
Rankings
Overall Rank
-
Coding Rank
-
Qwen2.5-7B Model Integrity Report
Total Score
64
/ 100
Audit Note
Qwen2.5-7B demonstrates strong transparency in its architectural design and licensing, providing clear technical specifications and a permissive Apache 2.0 license. However, the profile is weakened by a lack of disclosure regarding training compute resources and the specific composition of its 18-trillion-token dataset. While the model is highly accessible for deployment, the inability to verify its training data and compute footprint limits a full transparency assessment.
Upstream
Architectural Provenance
The model's architecture is thoroughly documented in the Qwen2.5 technical report and official blog posts. It is a dense, decoder-only transformer with specific details provided: 28 layers, 28 query heads, 4 KV heads (Grouped Query Attention), a hidden size of 3,584, and an intermediate size of 18,944. It utilizes SwiGLU activation, RMSNorm, and Rotary Position Embeddings (RoPE) with QKV bias. The transition from Qwen2 to Qwen2.5 is clearly defined as an evolution in training scale and data quality rather than a radical architectural shift.
Dataset Composition
While Alibaba discloses the total token count (18 trillion tokens) and general categories (web data, code, mathematics, and multilingual data in 29+ languages), there is no granular breakdown of the dataset's percentage composition. The documentation mentions 'high-quality' filtering using previous Qwen models as evaluators, but specific data sources, filtering thresholds, and the exact ratio of synthetic to organic data remain undisclosed. This lack of specificity prevents independent verification of the data's diversity and quality.
Tokenizer Integrity
The tokenizer is publicly accessible via the Hugging Face repository and is well-documented. It uses Byte Pair Encoding (BPE) with a large vocabulary of 151,643 tokens, optimized for multilingual support across 29+ languages. Technical details such as the handling of control tokens and the lack of 'unknown' words are explicitly stated. The tokenizer's efficiency for different languages (e.g., character-to-token ratios for English vs. Chinese) is also provided in the documentation.
Model
Parameter Density
The parameter count is clearly stated as 7.61 billion total, with 6.53 billion non-embedding parameters. As a dense model, all parameters are active during inference, which is explicitly confirmed in the technical documentation. The architectural breakdown (layers, heads, hidden dimensions) is fully transparent, allowing for precise calculation of parameter distribution across the model's components.
Training Compute
Information regarding the training compute is extremely limited. While the scale of the dataset (18T tokens) implies massive compute requirements, Alibaba has not disclosed the specific hardware used (e.g., number of H100/A100 GPUs), the total training duration in GPU hours, or the estimated carbon footprint. This lack of transparency regarding the environmental and financial costs of training is a significant gap.
Benchmark Reproducibility
Alibaba provides extensive benchmark results across standard sets like MMLU, GSM8K, and HumanEval. However, the exact evaluation prompts, few-shot examples, and specific code used to generate these scores are not fully public. While some evaluation frameworks like OpenCompass are mentioned, the lack of a comprehensive, one-click reproduction repository for the official scores makes independent verification difficult.
Identity Consistency
The model consistently identifies itself as Qwen, developed by Alibaba Cloud, in both its system prompts and documentation. It maintains clear versioning within the Qwen2.5 family (distinguishing between base, instruct, coder, and math variants). There are no reported instances of the model claiming to be a competitor's product or misrepresenting its fundamental identity.
Downstream
License Clarity
The Qwen2.5-7B model is released under the Apache 2.0 license, which is a standard, permissive open-source license allowing for both commercial and non-commercial use. The license terms are clearly stated in the Hugging Face repository and official announcements. Unlike the 3B and 72B variants which have custom 'Qwen License' restrictions for high-DAU applications, the 7B variant remains fully Apache 2.0.
Hardware Footprint
VRAM requirements for various precisions (FP16, INT8, INT4) are well-documented by both the provider and the community. Official documentation notes the 128K context window support and the resulting memory scaling. Third-party tools like Ollama and vLLM provide further clarity on the hardware needed to run the model at different quantization levels, though official documentation could be more explicit about the exact accuracy-performance tradeoffs of these quantizations.
Versioning Drift
The model follows a clear versioning scheme (Qwen2 -> Qwen2.5). However, there is limited public documentation regarding a formal changelog for minor weight updates or a structured policy for managing model drift. While major releases are well-announced, the process for silent updates or performance maintenance over time is not transparently defined.
GPU Requirements
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Context Size: 1,024 tokens