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Code Llama 7B Python

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Code Llama 7B Python is an open-weights language model from Meta, released August 2023. Its published size is 6.74B parameters and a 16k-token context window. Licence: llama2.

Catalogue identity notes

Maker repo codellama/CodeLlama-7b-Python-hf @d4178f5: licence tag llama2. 6,738,415,616 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2023-08: month the maker's Hugging Face repo was created (createdAt 2023-08-24). Python-specialised Code Llama (further trained on 100B Python tokens); not instruction-tuned. Ollama tags codellama:7b-python-q4_K_M, codellama:7b-python-q8_0 (sizes as listed on the tags page, observed 2026-09-29).

36.8±1.0#579 of 719 LLMsfrom 6 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of Code Llama 7B Python, out of 828 with a score, then more from Meta.

#modelscorewhereRAMreleased
670Granite 20B Code Base (8K) IBM37±1local~15GB2024-04
671Gemma 1.1 7B IT Google37±1local~7.1GB2024-04
672Code Llama 7B Python Meta37±1local~5.8GB2023-08
673Dolphin 2.2.1 Mistral 7B Cognitive Computations37±2local~6.1GB2023-10
674Code Llama 13B (base) Meta37±1local~9.2GB2023-08
more from Meta
24Muse Spark 1.3 Meta77±1api $2.00/M2026-09
40Muse Spark 1.2 Meta75±1api $2.00/M2026-08
43Muse Spark 1.1 Meta75±1api $2.00/M2026-07

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #398 of 458 · 79% of its score · 4 tests

Beats DeepSeek R1 Distill Qwen 7B, Qwen3.5 2B, DeepSeek R1 Distill Llama 8B here, though they rank higher overall. Trails Qwen2.5 1.5B Instruct, WizardLM 13B V1.2, Code Llama 7B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval +139.6% ✎▁█▄▅▇█·▄▇▅▄▄ beats 10/42easy · avg 78%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 63%sharp · at its level · several runs
MBPP lab47.6% ✎▃▃▃█··▃▅··▅▃ beats 3/11easy · avg 73%▇▇▇▇ 10%at its level
CRUXEval-I lab37.3% ✎▁··▁▅▂█▇▁··▁ beats 8/22medium · avg 54%▇ 3.0%at its level
CRUXEval-O lab35.9% ✎▁·▂▃▃█▁▂···▁ beats 7/22medium · avg 53%▇ 2.9%blunt · at its level
other tests1 not in a task · 20% of its score
testresultvs the others in its rundifficultycountswhy
EvoEval (overall)25.6% ✎▂▂▃▄▄█▁▄▅▃▁▃ beats 11/42medium · avg 55%▇▇▇▇▇▇▇▇ 20%at its level

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Code Llama 7B Python · needs ~4.1 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/CodeLlama-7b-Python-hf-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/CodeLlama-7b-Python-hf-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Code Llama 7B Python · needs ~4.1 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/CodeLlama-7b-Python-hf-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Code Llama 7B Python · needs ~4.1 GB · from ollama.com
    chat in the terminal
    ollama run codellama:7b-python-q4_K_M

Weights and documentation: huggingface.co/codellama ↗

$ how the score was worked out · Top Leaderboard Ranking = Top… 61%, Code Llama: Open Foundation M… 32%, CRUXEval published evaluation… 6%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM — authors' own runs2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 61%CC BY 4.0
Code Llama: Open Foundation Models for Code — authors' own runs · lab-reported2▇▇▇▇▇▇▇▇▇▇ 32%CC BY 4.0
CRUXEval published evaluation results (authors' generations and scores) · lab-reported2▇▇ 6%MIT
prior · pull toward the middle▇ 0%
weighted average= 36.8

$ improve this page

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Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json