# Atlas with Code Llama (local via Ollama) in 2026

> Code Llama (local via Ollama) offers a 16K tokens (16,384) context window and is available for Free (self-hosted) within Atlas.

Code Llama (local via Ollama) serves as a valuable baseline model for Atlas in 2026, particularly strong in fill-in-the-middle code infilling tasks. It is available for Free (self-hosted), making it an accessible option for local development and experimentation within Atlas, though its 16K tokens (16,384) context window presents limitations for complex agentic workflows.

## Key takeaways

- Code Llama (local via Ollama) is Free (self-hosted) for local use with Atlas, offering a cost-effective solution.
- It provides a 16K tokens (16,384) context window, which is often too small for complex agentic work in Atlas.
- The model is strong in fill-in-the-middle infilling, its explicit training focus, making it useful for specific code completions.
- Available in four sizes: 7B, 13B, 34B, and 70B, plus dedicated instruct and Python-tuned variants.
- Code Llama predates the agentic tool-calling era, making tool calls unreliable for Atlas's permission gate.
- For real Atlas agent work, prefer `ollama pull devstral` or `ollama pull qwen3-coder:30b` for better performance.

## What is Code Llama (local via Ollama) best at in Atlas?

Code Llama (local via Ollama) excels as a fill-in-the-middle specialist within Atlas, offering strong code infilling capabilities. Developers in 2026 can leverage its dedicated instruct and Python-tuned variants, available in four sizes from 7B to 70B, for targeted code completion tasks and as a foundational model.

Code Llama (local via Ollama) was explicitly trained for fill-in-the-middle infilling, making it a robust choice for completing code snippets or filling in missing sections directly within Atlas. Its availability in multiple sizes (7B, 13B, 34B, and 70B) and specialized variants allows for flexibility depending on local hardware and specific coding needs. Atlas users can benefit from its mature quantizations and broad tooling support, which have developed over years in the ecosystem. This model is particularly useful for quick, localized code completions where the task is well-defined and does not require extensive context or complex tool interactions. It serves as a solid baseline for understanding basic code generation capabilities within the Atlas environment.

## What are the cost and context tradeoffs for Code Llama (local via Ollama)?

Code Llama (local via Ollama) offers a compelling Free (self-hosted) pricing model, making it highly accessible for developers in 2026. However, its 16K tokens (16,384) context window is a significant limitation for Atlas's advanced agentic work, often proving too small for comprehensive codebase searches.

The primary advantage of Code Llama (local via Ollama) is its Free (self-hosted) cost, eliminating any per-token charges and allowing unlimited local usage. This makes it an excellent choice for experimentation and personal projects within Atlas, especially for those prioritizing cost efficiency. The main tradeoff, however, is its 16K tokens (16,384) context window. For an AI coding agent like Atlas, which performs Axis, the hybrid semantic and keyword code search fused by reciprocal rank fusion, this context size is often insufficient. Atlas's codebase search results alone can easily exceed 16K tokens, leading to truncated information and hindering the agent's ability to understand complex problems or large codebases. This limitation means that while the model is free, its utility for deep, agentic problem-solving is constrained.

## When should I choose a different model over Code Llama (local via Ollama) for Atlas?

For robust agentic work in Atlas in 2026, developers should generally prefer models like `ollama pull devstral` or `ollama pull qwen3-coder:30b` over Code Llama (local via Ollama). This is because Code Llama predates the agentic tool-calling era, making it unreliable for producing the well-formed tool calls required by Atlas's permission gate.

Code Llama (local via Ollama) is best treated as a completion baseline rather than a primary driver for advanced agentic tasks within Atlas. Its design predates the agentic tool-calling era, meaning it does not reliably produce the structured, well-formed tool calls that Atlas's permission-gated tool execution requires. Atlas's every tool call is permission-gated against allow, ask, and deny rules before it runs, and models that struggle with consistent tool calling will impede workflow. For scenarios demanding complex interactions, multi-step plans, or extensive code modifications, Atlas's ability to fan out work to subagents or build its code index with local Ollama embeddings will be better served by models specifically designed for tool use and larger contexts. Atlas's documentation explicitly recommends preferring `ollama pull devstral` or `ollama pull qwen3-coder:30b` for real agent work, as these models offer superior capabilities for modern AI coding agent workflows.

## Setup

1. Pull the desired Code Llama variant using Ollama. For example, to get the 13B model, run: `ollama pull codellama:13b` (or 7b, 34b, 70b for other sizes).
2. Register Code Llama in your `atlas.json` file within the Ollama provider block. This tells Atlas how to interact with the locally hosted model.
3. Set the context limit for Code Llama in your `atlas.json` configuration to its actual capacity of 16,384 tokens. This ensures Atlas respects the model's context window.
4. An example `atlas.json` snippet for configuring Code Llama (local via Ollama) would look like this:
5. {
  "providers": {
    "ollama": {
      "models": {
        "codellama:13b": {
          "limit": { "context": 16384 }
        }
      }
    }
  }
}
6. Select Code Llama (local via Ollama) from the `/models` interface within Atlas to make it your active model.
7. Treat Code Llama as a completion baseline for basic tasks. For advanced agent work, consider `ollama pull devstral` or `ollama pull qwen3-coder:30b` as recommended alternatives.

## FAQ

### Is Code Llama (local via Ollama) free to use with Atlas?

Yes, Code Llama (local via Ollama) is Free (self-hosted), meaning there are no direct costs associated with its usage within Atlas, beyond your local hardware and electricity. This makes it an accessible option for developers in 2026.

### What is the context window size for Code Llama (local via Ollama) in Atlas?

Code Llama (local via Ollama) provides a 16K tokens (16,384) context window when used with Atlas. This size can be limiting for complex agentic tasks, as Atlas's codebase search results alone can exceed it.

### Can Code Llama (local via Ollama) perform tool calls in Atlas?

Code Llama (local via Ollama) predates the agentic tool-calling era and does not reliably produce well-formed tool calls for Atlas's permission gate. It is not recommended for tasks requiring consistent tool interaction or complex agentic workflows.

### What are the available sizes for Code Llama (local via Ollama)?

Code Llama (local via Ollama) is shipped by Ollama in four distinct sizes: 7B, 13B, 34B, and 70B. Additionally, dedicated instruct and Python-tuned variants are available, offering flexibility for various use cases.

### Why is Code Llama (local via Ollama) considered a baseline model for Atlas?

Code Llama (local via Ollama) is considered a baseline due to its strong fill-in-the-middle infilling capabilities and free self-hosted nature. However, its 16K token context window and lack of reliable tool-calling support limit its effectiveness for advanced agentic workflows in Atlas, positioning it as a starting point rather than a primary driver.

### How do I configure Code Llama (local via Ollama) in Atlas?

To configure Code Llama (local via Ollama), first pull a variant like `codellama:13b` using Ollama. Then, register it in your `atlas.json` provider block with the specific context limit: `"limit": { "context": 16384 }`. Finally, select it from Atlas's `/models` interface to activate it.

### What models are recommended over Code Llama (local via Ollama) for agentic work in Atlas?

For real Atlas agentic work, it is recommended to prefer models like `ollama pull devstral` or `ollama pull qwen3-coder:30b` over Code Llama (local via Ollama). These alternatives offer better support for tool calling and often larger context windows, which are crucial for complex agentic tasks.

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