Code

Code Llama

A version of Llama 2 further trained on code, with variants for Python and for chat. Outdated, but many ready-made fine-tuned versions and tools exist.

The last open version came out in Jan 2024. The family has not been updated for a long time: the model still works, but do not expect fixes or new sizes.

Developer
Meta, USA
First release
Aug 2023
Latest release
Jan 2024
Sizes
7B – 70B
License
Commercial use with conditionsLlama 2 Community License (commercial use allowed with restrictions)
Russian
Not supported
Ready-made builds
GGUF
Running
Available in OllamaNeeds a GPU
Industries
Software development, Education
Deploy on your server Hugging Face GitHub

What it does

  • Code autocompletion and explanation
  • Generating Python scripts
  • Base model for fine-tuning on your own stack

Where it is used

IT departmentsDeveloper training

Hardware requirements

LaptopLaptop or regular PC, up to 8 GB of VRAM — smaller versions
fits
1 GPUOne GPU with 16–80 GB — mid-size versions
fits
ClusterServer with several GPUs — flagship versions
no versions

Versions

  1. Code Llama 70B
  2. Code Llama 7B / 13B / 34B

How to run it

With Ollama — the fastest wayInstall Ollama on your computer or server and find the model in its library by name. Good enough to try the model on your own tasks in one evening.Find it in the Ollama library
On your own serverWeights are downloaded from Hugging Face and served with vLLM (load and API) or llama.cpp (modest hardware). The model then runs in a closed network with no per-request fees.Hugging Face
How much hardware you needCalculate the VRAM for the model size, context length and number of concurrent requests.Open the hardware calculator

I can set this up end to end: pick the model size, deploy it on your server and connect it to your systems. Quantization compresses a model so it takes less video memory and runs on more modest hardware. Answers change slightly, so quality is checked on your own examples.

Frequently asked questions

Can Code Llama be used in a commercial project?

With conditions. License: Llama 2 Community License (commercial use allowed with restrictions). Restrictions vary — region, company revenue, attribution requirements. Have a lawyer check the terms before a commercial launch.

What hardware does Code Llama need?

At minimum: Laptop or regular PC, up to 8 GB of VRAM — smaller versions. Without a GPU the model is not practical. You can calculate the exact VRAM for your model size and context in the hardware calculator.

Does Code Llama support Russian?

No. The model card lists its languages and Russian is not among them.

Where can I download Code Llama and what does it cost?

The Code Llama weights are open and free to download. You only pay for the hardware it runs on and for the setup. Source links are at the bottom of this page.

How I deploy it for clients

  1. SelectionI pick the model size for your task and hardware and test it on your examples.
  2. DeploymentI deploy it on your server or in a closed network and provide an API.
  3. Fine-tuningI fine-tune it on your data (LoRA) or connect a knowledge base — whichever is cheaper for the task.
  4. IntegrationI connect it to your CRM, ERP, bot, website or team chat and set up monitoring.

Comparisons

Similar models

TextLlamaMeta · USACommercial use with conditions

The models that started mass open source in AI. A huge ecosystem of fine-tuned versions and tools.

DetailsCodeQwen CoderAlibaba (Qwen team) · ChinaCommercial use allowed

The broadest open coding family: from 0.5B for autocompletion to 480B for agents. Qwen3-Coder-Next (80B, 3B active) works as a developer agent on a single GPU.

DetailsCodeDeepSeek-CoderDeepSeek · ChinaCommercial use with conditions

DeepSeek's coding model family: from small autocompletion models to the large MoE V2, which matched closed models in 2024. Later, coding moved into DeepSeek's general models.

Details

Source: huggingface.co/codellama/CodeLlama-70b-Instruct-hf. Data checked against the model card on 22 Sep 2026. Have a lawyer review the license before commercial launch.