Code Llama Guide

---
name: code-llama-guide
description: Build with Meta's Code Llama — open code models for generation, infilling, and long-context code tasks.
category: ai-research
---

## Overview

Code Llama (Meta) is the code-specialized derivative of Llama — open-weight
models for code generation, completion, infilling, and instruction-following
code assistance, in sizes from 7B to 70B (plus a long-context variant). As a
Llama derivative, it inherits the ecosystem advantages: every provider hosts
it, tooling targets it, and deployment is frictionless.

For builders, Code Llama is the default open code model — the safe choice with
maximum support. It may not top every code benchmark against newer specialized
models (DeepSeek Coder, Qwen-Coder), but its combination of solid quality,
broad availability, and the Llama fine-tuning ecosystem makes it the baseline
against which open code models are judged.

The practical stance: start open-code evaluations with Code Llama as the
baseline; adopt alternatives when they beat it on your tasks.

## When to use

- Default open code model when you want maximum ecosystem support.
- Code completion and infilling (FIM support).
- Long-context code tasks (the long-context variant for repo-scale work).
- Code assistants and chat-based programming help (instruction-tuned).
- Fine-tuning code models (the Llama code ecosystem is mature).
- Python-heavy workloads (plus strong multi-language coverage).

## Core concepts

- **Size range (7B–70B)**: ladder from fast completion models to capable
  reasoning models. Match size to task: 7B/13B for completion, larger for
  complex generation.
- **Variants**: base (completion), Python-specialized, and instruct
  (chat/assistant). Choose per use case — don't use instruct for pure
  completion or base for chat.
- **Fill-in-the-middle**: infilling support for IDE completion. Test on your
  code patterns.
- **Long-context variant**: extended context for repository-level tasks.
  Validate on real repos.
- **Llama ecosystem**: providers, quantization, fine-tuning tools — the
  operational advantages of the standard.
- **Instruction tuning**: the instruct variants for assistant behavior;
  evaluate multi-turn code dialogue quality.
- **Multi-language**: strong across popular languages; per-language testing
  on your stack.
- **License**: the Llama community license terms — read for your scale and
  use case.

## Practical workflow

1. **Set Code Llama as the baseline.** Benchmark it first on your code tasks;
   alternatives must beat it to displace it.
2. **Match variant to use case.** Completion → base/FIM; assistant → instruct;
   repo tasks → long-context variant. Test each on its intended job.
3. **Ladder-test sizes.** 7B vs. 13B vs. 34B vs. 70B on your tasks. Completion
   often works at small sizes; complex generation needs larger.
4. **Test your languages.** Your stack's languages, your frameworks, your
   idioms — not just Python benchmarks.
5. **Evaluate infilling.** For IDE use: prefix/suffix completion on your
   codebase.
6. **Check the license.** Community license terms for your deployment scale.
7. **Monitor in production.** Acceptance rates for completion; task success
   for generation. Offline benchmarks don't capture the full picture.

Checklist for Code Llama in production:
- Baselined against alternatives; displacement justified by measurement.
- Correct variant per use case.
- Size right-sized via ladder testing.
- Your languages tested on your code.
- License confirmed; production quality monitored.

## Common pitfalls

- **Baseline skipped.** Adopting a newer code model without checking whether
  it actually beats Code Llama on your tasks.
- **Variant mismatch.** Instruct model for completion (worse + slower) or
  base model for chat (unhelpful). Match variant to job.
- **Oversizing.** 70B for simple completion. Right-size per task.
- **Python-only evals.** Assuming multi-language quality from Python scores.
  Test your languages.
- **FIM untested.** Completion deployment without infilling evaluation.
- **Long-context assumed.** The variant exists; your repo tasks still need
  testing.
- **License blindness.** Not reading the community license terms.
- **No production metrics.** Offline benchmarks without acceptance-rate
  tracking in the real tool.