How to Install CodeLlama in 2026 (Ollama Guide)

Can you still install CodeLlama in 2026, and should you? Short answer: yes, but the picture changed. Meta’s official CodeLlama repo was archived on July 1, 2025 – it’s read-only now, with no new releases coming. The weights still work fine, Ollama still serves them, and the model still writes solid code. But if you’re deploying an open source coding model today, you need to know what you’re actually installing.

This guide covers the Ollama install path (the one 90% of people actually use), the RAM math nobody explains correctly, and the fill-in-the-middle prompt trap that breaks half the tutorials online.

What CodeLlama actually is (and its current status)

Four model sizes – 7B, 13B, 34B, 70B. Each comes in three flavors: a base model, a Python specialist, and an instruction-tuned chat version. The base models started from Llama 2, then trained on 500 billion tokens of code; the Python variant got 100 billion additional tokens on top of that. All of this is documented in the Code Llama paper (arXiv:2308.12950) if you want the technical specifics.

Supported languages, per the Ollama library page: Python, C++, Java, PHP, TypeScript/JavaScript, C#, and Bash.

Here’s the part every 2024-vintage tutorial skips: the repository was archived by Meta on July 1, 2025 – it’s now read-only. And in the Ollama v0.32.0 release, CodeLlama, Qwen2.5-Coder, Llama 3.x, Mistral, StarCoder, and the base DeepSeek-R1 tags now trigger a deprecation warning before ollama launch continues. It still runs. Meta just isn’t updating it.

System requirements per model size

Ollama’s minimum requirement is 8 GB of RAM, 10 GB of free disk, and a 64-bit CPU with AVX2 – no GPU required. That’s the floor for the tool itself. CodeLlama’s actual footprint depends on which variant you pull and how it’s quantized.

The rule that matters: a Q4_K_M quantized model needs roughly 1.2x its file size in RAM during inference. That 34B sitting at ~19 GB on disk? Plan for ~24 GB free – not 24 GB total system RAM. The most common error message (model requires more system memory than is available) is almost always a free-RAM problem, not a total-RAM problem. Close Chrome before pulling 34B.

GPU is optional but changes everything. When a model exceeds VRAM, Ollama offloads excess layers to system RAM – and you’ll take a 5-20x speed hit when that happens. Running 13B on an 8 GB GPU means noticeable lag on longer completions.

Install and pull the model

Skip the source install. The archived Meta repo is a research reference now – for actual deployment, Ollama is the path.

Step 1 – Install Ollama. On macOS or Windows, grab the installer from ollama.com. On Linux:

curl -fsSL https://ollama.com/install.sh | sh

Step 2 – Verify Ollama is up.

ollama --version
curl http://localhost:11434/api/tags

The curl call should return JSON (empty models array is fine on a fresh install).

Step 3 – Pull CodeLlama. Pick your variant. The 7B Instruct is the sensible default for laptop-class hardware:

# General instruct model (recommended default)
ollama pull codellama:7b-instruct

# Python-specialized
ollama pull codellama:7b-python

# Code-completion variant (supports FIM)
ollama pull codellama:7b-code

# Larger sizes
ollama pull codellama:13b-instruct
ollama pull codellama:34b-instruct

The CodeLlama library page on Ollama showed 5.7M pulls as of mid-2025, with the last model update over a year prior – stable, but not moving.

First run and verification

Quick sanity check with an instruct model:

ollama run codellama:7b-instruct "Write a Rust function that reverses a linked list in place. Explain each step."

Coherent response with actual Rust syntax? You’re done. Hangs for 30+ seconds before generating a single token? You’re probably CPU-only – check with ollama ps while the model loads to see whether GPU layers are actually being used.

To use it programmatically via the local API:

curl http://localhost:11434/api/generate -d '{
 "model": "codellama:7b-instruct",
 "prompt": "Write a SQL query to find duplicate emails in a users table",
 "stream": false
}'

The fill-in-the-middle trap

Send a plain chat prompt to codellama:7b-code and you’ll get garbage output – not an error, just bad output. That’s the trap. The :code variants on 7B and 13B expect a specific FIM format: <PRE>, <SUF>, and <MID> tokens wrapping the prefix and suffix of the code you want completed. Without them, the model has no idea what you’re asking.

The exact format, using a GCD function as an example:

ollama run codellama:7b-code '<PRE> def compute_gcd(x, y): <SUF>return result <MID>'

Pro tip: If you want IDE-style completion without wrestling with FIM tokens yourself, hand it to the plugin layer. Tools like Continue or Tabby format the tokens automatically – you write code, they build the FIM prompt behind the scenes. For chat-style prompts like “explain this function,” use the :instruct tag instead.

Here’s a question worth sitting with: how long will a model frozen in July 2025 stay useful as newer codebases, frameworks, and language features accumulate? Nobody knows the answer yet – but it’s the right question to ask before building something long-lived on top of CodeLlama.

Common errors and how to fix them

“model requires more system memory than is available” – Free RAM shortage, not total. Close browsers and Slack, or drop to a smaller quant. Check with free -h on Linux or Activity Monitor on macOS before retrying.

Port 11434 returning “connection refused”? Community-reported common causes: something else already on that port, or the Ollama service didn’t start cleanly. Kill whatever’s on the port (lsof -i :11434 on Linux/Mac) or restart the Ollama service and try again.

Model “forgets” earlier context – Ollama’s default context window is small. Bump it:

ollama run codellama:13b-instruct --num-ctx 8192

One context quirk that’s easy to miss: per Meta’s docs, all CodeLlama variants except the 70B Python and Instruct versions support sequences up to 100,000 tokens. The 70B – the one people pull specifically for “more power” – has a shorter maximum context than the 7B. Not intuitive at all.

Very slow generation on a good GPU – Model is spilling to system RAM. Run ollama run codellama:7b --verbose and check the GPU layers count. Zero means CUDA/ROCm isn’t wired up.

Upgrade and uninstall

The model itself hasn’t been updated in over a year, so there’s nothing to upgrade on the CodeLlama side. To refresh Ollama itself, re-run the install script on Linux, or download the new installer on Mac/Windows – models are preserved.

To remove a specific CodeLlama variant:

ollama list
ollama rm codellama:34b

To wipe everything Ollama-related, uninstall the app and delete the models directory at ~/.ollama (or C:UsersYourName.ollama on Windows). That directory can grow well past 40 GB if you’ve been pulling multiple variants – worth checking before it fills a drive.

Should you pick CodeLlama in 2026?

Honest answer: it depends on why you want it. For a stable, well-understood open code model you can pin and forget, CodeLlama still delivers. The weights are licensed for commercial use under the Llama 2 community license, the paper is public, and the Ollama distribution just works.

But if you’re starting fresh and want the current best open coder, the community has largely moved on. Ollama’s own deprecation list groups CodeLlama with legacy agent models, and newer models like Qwen2.5-Coder ship at similar sizes with better benchmarks on recent tasks. Worth trying both on your actual codebase before committing to either.

FAQ

Is CodeLlama really deprecated?

The repo is archived and Ollama tags it as legacy, but the model still runs and inference works normally. Nothing breaks – you just won’t get updates.

Can I run codellama:34b on a 16 GB laptop?

Technically yes, practically no. The Q4_K_M file is around 19 GB on disk, and you need roughly 1.2x that in free RAM during inference – call it 24 GB minimum. On a 16 GB machine, Ollama will offload layers to disk swap and you’ll be looking at single-digit tokens per second. Stick to the 7B or 13B on a laptop.

What’s the difference between codellama, codellama:code, and codellama:instruct?

The base codellama tag is a foundational completion model – it continues code, but wasn’t trained to follow chat instructions. The :code variant is built for fill-in-the-middle completion inside an existing file, using the <PRE><SUF><MID> token format that IDE plugins call automatically. The :instruct tag is the fine-tuned chat version for question-and-answer prompts like “explain this function” or “write me a test.” If you’re not sure which to pick, start with :instruct. The :code tag will silently produce bad output if you send it a plain prompt – which is exactly the FIM trap described above.

Next step: Run ollama pull codellama:7b-instruct right now, feed it a real bug from your current project, and compare the output to whatever you’re currently using. Ten minutes of hands-on testing beats another hour reading tutorials.