[whatmodel.sh](../../README.md) / Code Llama 13B Instruct
# Code Llama 13B Instruct
Meta · released 2023-08 · 13.02B params · llama2
**score 40.2** ±0.7 · #563 of 732 · from 14 benchmark results across 5 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Code Llama 13B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #387 of 469 63% 6
math ▁▁▃●█▆▄█▄▂▁▁ #348 of 431* 3.8% 1
coding · #387 of 469 · 63% of its score
Beats [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Tulu 2 70B](tulu-2-70b.md) here, though they rank higher overall. Trails [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md), [WizardLM 13B V1.2](wizardlm-13b-v1-2.md), [Falcon3 3B Instruct](falcon3-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 48.8% ▁█▄▅▇●·▄▇▅▄▄ beats 19/42 easy · avg 75% 36% sharp · at its level · several runs
HumanEvalSynthesize (HumanEvalPac… 39.4% ▁·▂▁▂▆▅●▄▄▁▂ beats 17/35 medium · avg 62% 8.4% at its level
MBPP (maker) 49.4% ▃▃▃●··▃▅··▅▃ beats 5/11 easy · avg 71% 6.1% at its level
HumanEvalExplain (HumanEvalPack, … 27.7% ▄▇█▃▄●▄▁▇▃·▁ beats 23/35 medium · avg 46% 4.9% typical
HumanEvalFix (HumanEvalPack, 6-la… 15.8% ▁·▁▁▄█●▄█▁▅▅ beats 15/35 medium · avg 51% 4.5% sharp
Tulu 2: CodexEval pass@10 69.2% ▃▃▃·▆▃▃●▃▂▂▅ beats 17/25 easy · avg 83% 3.4% sharp · at its level
math · #348 of 431 · 3.8% of its score
Beats [Athene-70B](athene-70b.md), [Gemma 2 9B IT](gemma-2-9b.md), [GPT-3.5 Turbo (0125)](gpt-3-5-turbo-0125.md) here, though they rank higher overall. Trails [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md), [Zephyr 7B beta](zephyr-7b-beta.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K 23.0% ▆▂●█▅▅▃▆···▂ beats 6/25 easy · avg 72% 3.8% sharp · at its level
other tests (6)
test result vs the others in its run difficulty counts why
EvoEval (overall) 33.8% ▂▂▃▄▄●▁▄▅▃▁▃ beats 23/42 medium · avg 52% 12% at its level
BIG-Bench Hard 48.0% ▁·····▂●▂▅·▁ beats 7/25 easy · avg 74% 6.9% sharp · at its level
BigCodeBench (Complete) 29.7% ▂▁▁●▂▅▇█▆▂▂▂ beats 11/58 medium · avg 48% 5.1% at its level
BigCodeBench (Instruct) 28.2% ▃▂·●▆██▆▅▃▃▃ beats 4/33 medium · avg 39% 4.8% at its level
MMLU 43.3% ▁▁▃●▄█▄▁█··▁ beats 4/25 medium · avg 73% 3.1% at its level
Tulu 2: TyDiQA gold passage, 1-sh… 37.8% ▂··▁●▂▂▂█▅▁▂ beats 4/25 medium · avg 59% 0.8% blunt · at its level
source results share of the score
Top Leaderboard Ranking = Top Coding Pr… 2 35% ▇▇▇▇
Code Llama: Open Foundation Models for … 2 19% ▇▇
Tulu 2 5 18% ▇▇
Granite Code Models: A Family of Open F… 3 18% ▇▇
BigCodeBench paper 2 10% ▇
prior: pull toward the middle 0% ▇
weighted average = 40.2
Licences: Top Leaderboard Ranking = Top Coding Proficiency ([CC BY 4.0](../../licenses/paper-2403.19114v1-CC-BY-4.0.txt)) · Code Llama: Open Foundation Models for Code ([CC BY 4.0](../../licenses/paper-2308.12950v3-CC-BY-4.0.txt)) · Tulu 2 ([CC BY 4.0](../../licenses/CC-BY-4.0.txt)) · Granite Code Models: A Family of Open Foundation Models for Code Intelligence ([CC BY 4.0](../../licenses/paper-2405.04324v1-CC-BY-4.0.txt)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Code Llama 13B Instruct (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/tree/82f1dd9567b9b20b7e8f8aa9ecf3d2f121e5d415))
```sh
llama cli -hf TheBloke/CodeLlama-13B-Instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/CodeLlama-13B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~13.8 GB) `llama cli -hf TheBloke/CodeLlama-13B-Instruct-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Code Llama 13B Instruct (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF/tree/82f1dd9567b9b20b7e8f8aa9ecf3d2f121e5d415))
```sh
lms get https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~13.8 GB) `lms get https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-GGUF@Q8_0 -y` · 8bit (~13.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/CodeLlama-13b-Instruct-hf-8bit-mlx -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Code Llama 13B Instruct (Q4_K_M, needs ~7.9 GB, from [ollama.com](https://ollama.com/library/codellama/tags))
```sh
ollama run codellama:13b-instruct-q4_K_M
```
Other versions: Q8_0 (~14 GB) `ollama run codellama:13b-instruct-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Code Llama 13B Instruct (8bit, needs ~13.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/CodeLlama-13b-Instruct-hf-8bit-mlx/tree/e87538682c2189e6cd58adfe7a975db7a3438ca5))
```sh
mlx_lm.chat --model mlx-community/CodeLlama-13b-Instruct-hf-8bit-mlx
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/CodeLlama-13b-Instruct-hf-8bit-mlx
```
Model repository and documentation: [huggingface.co/codellama](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf)
[interactive version](https://whatmodel.sh/model/codellama-13b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)