whatmodel.sh / WizardCoder Python 13B V1.0

WizardCoder Python 13B V1.0

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WizardCoder Python 13B V1.0 is an open-weights language model from WizardLM, released August 2023. Its published size is 13B parameters and a 16k-token context window. Licence: llama2.

Catalogue identity notes

Maker repo WizardLM/WizardCoder-Python-13B-V1.0 @5ac6748: licence tag llama2. Parameters: nominal size from the model name (no safetensors metadata in the repo) (dense, so active = total). Released 2023-08: month the maker's Hugging Face repo was created (createdAt 2023-08-26). Evol-Instruct fine-tune of Code Llama 13B Python. Ollama tags wizardcoder:13b-python-q4_K_M, wizardcoder:13b-python-q8_0 (sizes as listed on the tags page, observed 2026-09-29).

41.9±2.4#501 of 719 LLMsfrom 3 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of WizardCoder Python 13B V1.0, out of 828 with a score, then more from WizardLM.

#modelscorewhereRAMreleased
590Llama 3 8B Instruct Meta42±0local~6.6GB2024-04
591WizardLM 70B V1.0 Microsoft42±1local~32GB2023-08
592WizardCoder Python 13B V1.0 WizardLM42±2local~9.8GB2023-08
593Hermes 3 Llama 3.1 8B Nous Research42±1local~6.6GB2024-07
594Falcon2 11B (base) TII42±3local~8.2GB2024-05
more from WizardLM
424WizardCoder 33B V1.1 WizardLM50±1local~22GB2024-01
511WizardCoder Python 34B V1.0 WizardLM46±1local~23GB2023-08
600WizardCoder-15B-V1.0 WizardLM42±1local~12GB2023-06

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #347 of 458 · 97% of its score · 3 tests

Beats LFM2.5 2.6B, LFM2.5 8B-A1B, Qwen3-VL 2B Thinking 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 lab64.0% ✎▁▁▁▁▁▃▅▅█▃▂▁ beats 105/190easy · avg 82%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 57%sharp · at its level · small run
CRUXEval-I36.5% ✎▁··▁▅▃█▇▁··▁ beats 7/22medium · avg 54%▇▇▇▇▇▇▇▇ 21%at its level
CRUXEval-O41.3% ✎▁·▂▃▁█▁▂···▁ beats 13/22medium · avg 53%▇▇▇▇▇▇▇▇ 20%blunt · 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 WizardCoder Python 13B V1.0 · needs ~7.9 GB · from huggingface.co/TheBloke
    chat in the terminal
    llama cli -hf TheBloke/WizardCoder-Python-13B-V1.0-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf TheBloke/WizardCoder-Python-13B-V1.0-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 WizardCoder Python 13B V1.0 · needs ~7.9 GB · from huggingface.co/TheBloke

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-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 WizardCoder Python 13B V1.0 · needs ~7.9 GB · from ollama.com
    chat in the terminal
    ollama run wizardcoder:13b-python-q4_K_M

Official weights and documentation: huggingface.co/WizardLM ↗

$ how the score was worked out · WizardLM team vendor-reported… 57%, CRUXEval published evaluation… 40%

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
WizardLM team vendor-reported scores for its own WizardCoder models (Hugging Face card) · lab-reported1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 57%Factual vendor-reported score (not licensed content)
CRUXEval published evaluation results (authors' generations and scores)2▇▇▇▇▇▇▇▇▇▇▇▇ 40%MIT
prior · pull toward the middle▇ 3%
weighted average= 41.9

$ 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