# Atlas with Qwen2.5-Coder 7B (Ollama) in 2026 > Qwen2.5-Coder 7B (Ollama) offers a 32K token context window and is available for free as a self-hosted solution. Atlas with Qwen2.5-Coder 7B (Ollama) provides a robust, fully offline AI coding experience, excelling at single-file edits and unified diff review. This model is self-hosted and free, offering a 32K token context window for developers in 2026. ## Key takeaways - Qwen2.5-Coder 7B (Ollama) is free and self-hosted, ideal for local development in 2026. - It offers a 32K token context window (32,768), suitable for single-file edits and unified diff review. - The model requires a 4.7GB download and runs on 8GB consumer GPUs or 16GB Apple Silicon machines. - It is the best-tested path in the Ollama ecosystem for a fully offline Atlas setup. - Expect context compaction on large repositories due to the 32K token limit filling fast. - For long agentic chains and multi-step plan-then-build runs, consider larger models like 14B or 32B. ## What is Qwen2.5-Coder 7B (Ollama) best for with Atlas? Qwen2.5-Coder 7B (Ollama) is an excellent choice for developers in 2026 seeking a fully offline Atlas setup, particularly for single-file edits and reviewing unified diffs. This 7B parameter model handles these tasks efficiently without requiring any cloud keys. This local model from Alibaba Qwen is specifically optimized for scenarios where code privacy and self-hosting are paramount. Within Atlas, Qwen2.5-Coder 7B (Ollama) shines when performing single-file edits, allowing developers to iterate quickly and review changes. It is also highly effective for processing and approving unified diffs, a core Atlas capability that surfaces every file edit for approval before writing. By leveraging local Ollama embeddings, Atlas can build its code index using AST declarations from tree-sitter, ensuring that your code never leaves your machine. This setup supports Atlas's Axis, the hybrid semantic and keyword retrieval fused by reciprocal rank fusion, entirely offline, making it ideal for sensitive projects. ## How does Qwen2.5-Coder 7B (Ollama)'s 32K context window perform? The Qwen2.5-Coder 7B (Ollama) model provides a substantial 32K token context window, which is often sufficient for handling a handful of source files and Atlas tool output within a single agent turn. However, developers should anticipate context compaction on larger repositories. With a precise context window of 32,768 tokens, Qwen2.5-Coder 7B (Ollama) can hold a significant amount of information. This capacity is generally ample for common Atlas operations, such as loading several relevant source files, processing Atlas tool output, and managing agent interactions within a single turn. However, it is important to note that this 32K context window can fill rapidly once Atlas injects tree-sitter chunks and diff context, especially when working on larger codebases. In such cases, developers should expect Atlas to perform context compaction to manage the available token space, which might impact the model's ability to retain very long-term memory across many turns without explicit summarization. ## What are the cost and hardware requirements for Qwen2.5-Coder 7B (Ollama) with Atlas? Qwen2.5-Coder 7B (Ollama) is entirely free to use as a self-hosted solution, making it a highly accessible option for developers in 2026. It requires a 4.7GB download and can run comfortably on an 8GB consumer GPU or a 16GB Apple Silicon machine. One of the most compelling advantages of Qwen2.5-Coder 7B (Ollama) is its pricing: Free (self-hosted). This eliminates ongoing API costs, providing a predictable and zero-cost operational model for developers. Regarding hardware, the model has a manageable footprint, requiring 4.7GB on disk for the download. To serve the model, approximately 7GB of memory is needed. This makes it comfortably runnable on consumer-grade hardware, specifically an 8GB consumer GPU or a 16GB Apple Silicon machine. This accessibility ensures that a wide range of developers can leverage Atlas with a powerful local AI agent without significant upfront or recurring infrastructure investments. ## When should I consider a different model than Qwen2.5-Coder 7B (Ollama)? While Qwen2.5-Coder 7B (Ollama) excels in many areas, developers in 2026 should consider alternative models for long agentic chains, particularly multi-step plan-then-build runs. This 7B model is weaker in such scenarios compared to its 14B or 32B counterparts. Qwen2.5-Coder 7B (Ollama) is a strong performer for focused tasks, but its primary tradeoff lies in its performance on more complex, multi-step agentic workflows. When Atlas needs to execute long agentic chains, such as intricate plan-then-build runs that involve multiple iterations of planning, tool execution, and refinement, this 7B model may exhibit weaker performance compared to larger models like the 14B or 32B versions of Qwen2.5-Coder. For developers tackling highly complex projects requiring extensive, multi-turn reasoning and execution, exploring models with greater capacity for agentic depth might yield better results, even if it means higher resource consumption or cloud dependency. ## Why is Qwen2.5-Coder 7B (Ollama) a recommended starting point for Atlas? Qwen2.5-Coder 7B (Ollama) is the default `qwen2.5-coder` tag and the most common starting point for a fully offline Atlas setup in 2026. It represents the best-tested path within the Ollama ecosystem, ensuring a smooth integration for developers. This model is the tag Ollama aliases to `qwen2.5-coder:latest`, making it the most widely adopted and best-tested path in the Ollama ecosystem. This ensures a stable and well-supported experience for developers integrating it with Atlas. Its ability to handle single-file edits and unified-diff review without any cloud key in the loop makes it an ideal foundation for a fully offline Atlas configuration. Furthermore, Atlas can be configured to build its codebase index using local Ollama embeddings, ensuring that Axis, the hybrid semantic and keyword code search, never transmits any source code to third-party servers, reinforcing privacy and security for your development workflow. ## Setup 1. 1: Pull the default Qwen2.5-Coder 7B tag: `ollama pull qwen2.5-coder:7b`. This model is 4.7GB and is aliased to `:latest`. 2. 2: Start the Ollama server and verify it is answering requests on `http://localhost:11434/v1`. 3. 3: Declare the Ollama provider in your `atlas.json` configuration against `@ai-sdk/openai-compatible`. List `qwen2.5-coder:7b` under models, setting `limit.context` to `32768` and `limit.output` to `8192`. 4. 4: For a single-model, fully offline configuration, set both `model` and `small_model` to `ollama/qwen2.5-coder:7b` in your Atlas configuration. 5. 5: Configure the codebase index to embed through Ollama. This ensures that Atlas's Axis, the hybrid semantic plus keyword search, never ships a chunk of source code to a third party. ## FAQ ### What is the context window size for Qwen2.5-Coder 7B (Ollama)? Qwen2.5-Coder 7B (Ollama) provides a 32K token (32,768) context window. This capacity is generally sufficient for many Atlas operations, including reviewing multiple source files and tool outputs within a single agent turn. ### Is Qwen2.5-Coder 7B (Ollama) free to use with Atlas? Yes, Qwen2.5-Coder 7B (Ollama) is entirely free to use as a self-hosted model. This makes it a highly cost-effective solution for developers in 2026 who prioritize local execution and zero recurring costs. ### What hardware is needed to run Qwen2.5-Coder 7B (Ollama) locally? To run Qwen2.5-Coder 7B (Ollama) comfortably, you will need an 8GB consumer GPU or a 16GB Apple Silicon machine. The model itself is a 4.7GB download and requires approximately 7GB of memory to serve. ### Can Qwen2.5-Coder 7B (Ollama) handle multi-step agentic workflows in Atlas? Qwen2.5-Coder 7B (Ollama) is weaker at long agentic chains, such as multi-step plan-then-build runs, compared to larger models like the 14B or 32B versions. For complex, multi-turn tasks, a more powerful model might be preferable. ### How does Atlas ensure my code stays private when using Qwen2.5-Coder 7B (Ollama)? When configured with Qwen2.5-Coder 7B (Ollama) and local Ollama embeddings, Atlas keeps your code entirely off third-party servers. The codebase index, used by Axis, the hybrid semantic and keyword code search, is built and queried locally, ensuring privacy. ### What are the specific steps to set up Qwen2.5-Coder 7B (Ollama) with Atlas? First, pull the model using `ollama pull qwen2.5-coder:7b`. Then, ensure your Ollama server is running. Configure your `atlas.json` to declare the Ollama provider and set `qwen2.5-coder:7b` as both `model` and `small_model`, specifying `limit.context 32768` and `limit.output 8192`. Finally, configure local Ollama embeddings for your codebase index. --- Canonical HTML: https://seashell.sh/resources/models/qwen2-5-coder-7b-local Source of truth: aeo_pages row `/resources/models/qwen2-5-coder-7b-local` (segment: Models) (this file is generated from it, never hand-edited). Licence: SeaShell is proprietary with a free core. It is not open source and there is no public source repository.