Why it matters
- Open-source and self-hostable — run code AI on private codebases without sending code to external APIs; critical for proprietary or regulated environments.
- Free commercial use under the Llama 2 Community License — eliminate per-token API costs at scale by hosting on your own GPU infrastructure.
- Code infilling capability fills in code between two existing blocks — useful for completing functions in context, not just generating from prompts.
- 100K token context window on larger models — process entire files, full functions, and multi-module context in a single inference call.
Key capabilities
- Code generation: Generate code from natural language descriptions in 500+ languages.
- Code completion: Autocomplete partial code; integrate with LSP-compatible editors.
- Infilling: Fill-in-the-middle (FIM) — generate code between a prefix and suffix context block.
- Instruction following: Code Llama Instruct variant handles chat-style "write me a function that…" prompts.
- Python specialization: Code Llama Python variant shows stronger benchmark performance on Python tasks.
- Debugging: Explain bugs, suggest fixes, and identify issues in provided code snippets.
- Code explanation: Describe what code does; generate documentation from code.
- Multiple sizes: 7B (fast, local CPU-feasible), 13B, 34B, 70B — choose speed vs quality tradeoff.
Technical notes
- Model sizes: 7B, 13B, 34B, 70B parameters
- Variants: Code Llama (base), Code Llama Python, Code Llama Instruct
- Context window: 4K tokens base; 100K for long-context versions
- Languages: Python, C++, Java, PHP, TypeScript, C#, Bash, 500+ total
- Base model: Built on Llama 2
- License: Llama 2 Community License (free commercial use for most)
- Download: HuggingFace —
meta-llama/CodeLlama-*
- Inference: Ollama, llama.cpp, vLLM, Together AI, Replicate
- GPU requirement: 7B: 8GB VRAM; 13B: 16GB; 34B: 40GB+; 70B: 80GB+
- Released: August 2023 (initial); January 2024 (Code Llama 70B)
Usage example
# Via Ollama (local inference)
# ollama pull codellama:34b
import ollama
response = ollama.chat(model='codellama:34b', messages=[
{'role': 'user', 'content': 'Write a Python function to parse a JSON config file with error handling.'}
])
print(response['message']['content'])
# Via Together AI (hosted API — OpenAI-compatible)
from openai import OpenAI
client = OpenAI(
api_key="YOUR_TOGETHER_API_KEY",
base_url="https://api.together.xyz/v1"
)
response = client.chat.completions.create(
model="togethercomputer/CodeLlama-34b-Instruct",
messages=[{"role": "user", "content": "Explain this Python code: def fib(n): return n if n <= 1 else fib(n-1) + fib(n-2)"}]
)
Ideal for
- Teams with private codebases who need self-hosted code AI without data leaving their environment.
- Organizations running high-volume code generation who want to eliminate per-token API costs.
- Researchers and developers fine-tuning a code model on domain-specific languages or internal codebases.
- Edge/embedded deployments where Code Llama 7B runs on consumer GPUs or quantized on CPU.
Not ideal for
- Teams wanting a fully managed, zero-infrastructure code assistant — use GitHub Copilot or Cursor instead.
- Cutting-edge reasoning or complex multi-step code architecture — GPT-4o or Claude 3.5 Sonnet typically outperform Code Llama on complex tasks.
- Non-technical users who need a chat interface rather than model weights.
See also
- StarCoder — BigCode/HuggingFace open code model; 600+ languages; alternative to Code Llama.
- Tabby — Self-hosted coding assistant server that can run Code Llama and StarCoder models.
- Ollama — Local model runner;
ollama pull codellama for instant local Code Llama setup.