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Launch a dedicated cloud GPU server running Laboratory OS to download and run Qwen 2.5 Coder 7B using any compatible app or framework.
Must be used with a compatible app, notebook, or codebase. May run slowly, or not work at all, depending on local system resources, particularly GPU(s) and available VRAM.
Open WebUI is an open-source, self-hosted web interface with a polished, ChatGPT-like user experience for interacting with LLMs. Integrates seamlessly with local Ollama installation.
The most full-featured web interface for experimenting with open source Large Language Models. Featuring a wide range of configurable settings, inference engines, and plugins.
Qwen2.5-Coder-7B is a 7.61 billion parameter transformer-based language model developed by Alibaba Cloud's Qwen Team, specialized for code generation and reasoning across 92 programming languages. The model features a 128,000-token context window, supports fill-in-the-middle code completion, and was trained on 5.5 trillion tokens of code and text data, demonstrating competitive performance on coding benchmarks like HumanEval and mathematical reasoning tasks.
Qwen2.5-Coder-7B is a large language model (LLM) developed by the Qwen Team at Alibaba Group, designed to address complex code generation, reasoning, and repair across a wide spectrum of programming languages. It is a member of the Qwen2.5-Coder model family, which serves as a successor to the CodeQwen1.5 series, and specifically targets high-performance coding tasks, while also retaining advanced mathematical and general reasoning abilities. As an open-source model, Qwen2.5-Coder-7B incorporates architectural innovations and demonstrates notable performance in coding benchmarks, providing a foundation for automated code understanding and generation.
Official logo representing the Qwen3 and Qwen2.5-Coder model family from Alibaba.
Qwen2.5-Coder-7B employs a transformer-based architecture enhanced with several advanced features, including Rotary Position Embeddings (RoPE), SwiGLU activation, and Root Mean Square Normalization (RMSNorm). This model uses 28 transformer layers and contains approximately 7.61 billion parameters, with 6.53 billion attributed to non-embedding layers. It utilizes separate attention heads for query and key-value processing, allocating 28 heads for queries and 4 for key-value pairs, a configuration described in the Qwen2.5-Coder Technical Report.
A significant aspect of Qwen2.5-Coder-7B is its extensive context window, supporting up to 128,000 tokens per prompt, made possible by the YaRN position encoding extension. This allows for handling of large documents, repository-wide code completion, and long-range code understanding. The model is trained to interpret and process code in 92 programming languages and includes specialized support for code completion methodologies such as fill-in-the-middle (FIM), using unique prompt tokens to designate task structure.
Qwen2.5-Coder-7B has been trained on a vast corpus comprising 5.5 trillion tokens, consisting of source code, synthetic data, and grounded text-code datasets. The scale and diversity of the training material are detailed in the Qwen2.5-Coder Technical Report, which emphasizes the inclusion of multi-programming language data and comprehensive mathematical benchmarks. The training regime incorporates post-training strategies such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), contributing to instruction-following and generalization.
Instruction-tuned variants, denoted as "Qwen2.5-Coder-Instruct", are further optimized with high-quality instruction data, enhancing capabilities in following detailed prompts and achieving user-aligned responses. This fine-tuning approach aligns with modern practices for instruction adaptation in language models aimed at interactive and assistant-like use cases.
Qwen2.5-Coder-7B demonstrates competitive results on a broad array of code-centric and general reasoning benchmarks. In studies, it achieves high scores in code generation and completion tasks on industry-standard datasets such as HumanEval, EvalPlus, MultiPL-E, and BigCodeBench. The model's instruction-tuned variant exhibits multilingual proficiency in practical coding scenarios, as evaluated by McEval.
Radial bar chart comparing Qwen2.5-Coder’s performance to other models across multiple coding benchmarks such as HumanEval, EvalPlus, and MultiPL-E.
Quantitative comparisons indicate Qwen2.5-Coder-7B-Base performs strongly in a variety of tasks, including those where it achieves scores comparable to or exceeding some larger open-source models in areas such as code completion, fill-in-the-middle generation, and mathematical problem solving.
Tabular comparison showing Qwen2.5-Coder 7B-Base’s scores across numerous code evaluation tasks relative to peer models.
Notably, the instruction-tuned version achieves high marks on code reasoning datasets, such as CRUXEval, and performs well in mathematical benchmarks like MATH and AMC23. Its multilingual capabilities extend to more than 40 languages, with sustained strong performance across both common and less frequently encountered programming environments.
In performance scaling, Qwen2.5-Coder models demonstrate a favorable performance-to-size ratio, performing effectively in code reasoning and instruction-following at smaller parameter scales.
Scatter plot illustrating Qwen2.5-Coder-Instruct’s performance in code reasoning benchmarks in relation to model size.
The versatility of Qwen2.5-Coder-7B enables its deployment for a wide range of software engineering applications. Its core use cases include automated code completion—at both file and repository granularity—with dedicated support for fill-in-the-middle code generation utilizing specialized FIM tokens. This feature is grounded in the efficient fill-in-the-middle methodology, allowing the insertion of code segments within complex codebases.
Furthermore, the model’s understanding of repository context, assisted by prompt tokens designating repository and file separators, makes it suitable for agent-based code suggestion tools and automated refactoring systems. While the base model is primarily aimed at non-conversational code tasks, the instruction-tuned versions are adapted for chat-based interfaces, enabling use as an interactive code assistant or integrated development environment assistant.
Qwen2.5-Coder-7B is part of the broader Qwen2.5-Coder series, which includes models ranging from 0.5 billion to 32 billion parameters. The series succeeded the CodeQwen1.5 line and is positioned within the larger Qwen family of language and multimodal models from Alibaba, which span natural language generation, vision, audio, and agent functionalities. The 7B model was officially released on September 19, 2024, with larger variants, including the 32B parameter class, designed to provide similar capabilities to some proprietary LLMs.
While Qwen2.5-Coder-7B is effective in code and reasoning tasks, the base model is not recommended for conversational or general dialogue use, except in its instruction-tuned variants. Handling of long-context prompts relies on YaRN-based positional scaling; some interfaces (such as vLLM) currently implement only static scaling, which may affect performance on short inputs if global scaling is enabled.
Qwen2.5-Coder-7B is made available under the open Apache 2.0 license, supporting transparent research, development, and commercial deployment.