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Code Llama 34B Python

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Code Llama 34B Python is an open-weights language model from Meta, released August 2023. Its published size is 33.74B parameters and a 16k-token context window. Licence: llama2.

Catalogue identity notes

Maker repo codellama/CodeLlama-34b-Python-hf @7daa5d7: licence tag llama2. 33,743,970,304 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; not instruction-tuned. Ollama tags codellama:34b-python-q4_K_M, codellama:34b-python-q8_0 (sizes as listed on the tags page, observed 2026-09-29).

41.0±0.9#514 of 719 LLMsfrom 6 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of Code Llama 34B Python, out of 828 with a score, then more from Meta.

#modelscorewhereRAMreleased
604InternVL2-4B Shanghai AI Laboratory (OpenGVLab)41±2local~10GB2024-07
605Qwen1.5 14B Chat Alibaba41±1local~11GB2024-02
606Code Llama 34B Python Meta41±1local~23GB2023-08
607speechless-coding-7b-16k-tora uukuguy41±1local~16GB2023-10
608DeepSeek-Coder 33B Base DeepSeek41±1local~22GB2023-10
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▁▁▁▃▆██▇▆▄▃▁ #354 of 458 · 77% of its score · 4 tests

Beats LFM2.5 8B-A1B, Qwen3-VL 2B Thinking, DeepSeek R1 Distill Qwen 7B here, though they rank higher overall. Trails Phi-3-mini-128k-instruct (Apr 2024), Code Tulu 2 13B, DeepSeek LLM 67B Chat here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval +152.4% ✎▁█▄▅▇█·▄▇▅▄▄ beats 24/42easy · avg 78%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 62%sharp · at its level · several runs
MBPP lab56.2% ✎▃▃▃█··▃▅··▅▃ beats 7/11easy · avg 73%▇▇▇▇ 10%at its level
CRUXEval-I lab43.9% ✎▁··▁▅▂█▇▁··▁ beats 16/22medium · avg 54%▇ 3.1%at its level
CRUXEval-O lab41.4% ✎▁·▂▃▁█▁▂···▁ beats 14/22medium · avg 53%▇ 2.9%blunt · at its level
other tests1 not in a task · 22% of its score
testresultvs the others in its rundifficultycountswhy
EvoEval (overall)32.2% ✎▂▂▃▄▄█▁▄▅▃▁▃ beats 18/42medium · avg 55%▇▇▇▇▇▇▇▇▇ 22%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 34B Python · needs ~20.2 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/CodeLlama-34b-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-34b-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 34B Python · needs ~20.2 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/CodeLlama-34b-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 34B Python · needs ~20 GB · from ollama.com
    chat in the terminal
    ollama run codellama:34b-python-q4_K_M

Weights and documentation: huggingface.co/codellama ↗

$ how the score was worked out · Top Leaderboard Ranking = Top… 62%, Code Llama: Open Foundation M… 31%, 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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 62%CC BY 4.0
Code Llama: Open Foundation Models for Code — authors' own runs · lab-reported2▇▇▇▇▇▇▇▇▇ 31%CC BY 4.0
CRUXEval published evaluation results (authors' generations and scores) · lab-reported2▇▇ 6%MIT
prior · pull toward the middle▇ 0%
weighted average= 41.0

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

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