whatmodel.sh / WizardCoder-15B-V1.0

WizardCoder-15B-V1.0

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WizardCoder-15B-V1.0 is an open-weights language model from WizardLM, released June 2023. Licence: bigscience-openrail-m.

Catalogue identity notes

Maker repo WizardLMTeam/WizardCoder-15B-V1.0 @9c17758 (requested as WizardLM/WizardCoder-15B-V1.0, which the HF API redirects to WizardLMTeam/WizardCoder-15B-V1.0): licence tag bigscience-openrail-m. Released 2023-06: month the maker's Hugging Face repo was created (createdAt 2023-06-14).

41.5±0.8#508 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of WizardCoder-15B-V1.0, out of 828 with a score, then more from WizardLM.

#modelscorewhereRAMreleased
598Ministral 3B (2410) Mistral42±1comparison only2024-10
599OLMo 2 7B Instruct (1124) Allen Institute for AI42±1local~5.8GB2024-12
600WizardCoder-15B-V1.0 WizardLM42±1local~12GB2023-06
601Snowflake Arctic Instruct Snowflake41±2local~1007GB2024-04
602Phi-3-vision-128k-instruct Microsoft41±2local~10GB2024-05
more from WizardLM
424WizardCoder 33B V1.1 WizardLM50±1local~22GB2024-01
511WizardCoder Python 34B V1.0 WizardLM46±1local~23GB2023-08
592WizardCoder Python 13B V1.0 WizardLM42±2local~9.8GB2023-08

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #355 of 458 · 100% of its score · 3 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
HumanEval56.7% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 44/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 53%sharp · at its level
HumanEval+50.6% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 43/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 39%sharp · at its level
MBPP+54.2% ✎▁···▁▁▂▅▇█▆▂ beats 24/75easy · avg 68%▇▇▇ 7.0%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-15B-V1.0 · needs ~10.1 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/WizardCoder-15B-V1.0-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/WizardCoder-15B-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-15B-V1.0 · needs ~10.1 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/WizardCoder-15B-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-15B-V1.0 · needs ~10.1 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/WizardCoder-15B-V1.0-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~31.04 GB · from huggingface.co/WizardLMTeam (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='WizardLMTeam/WizardCoder-15B-V1.0', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/WizardLMTeam ↗

$ how the score was worked out · EvalPlus / EvalPerf published… 100%

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
EvalPlus / EvalPerf published evaluation runs3▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 100%Apache-2.0
prior · pull toward the middle▇ 0%
weighted average= 41.5

$ 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