whatmodel.sh / WaveCoder-Ultra-6.7B

WaveCoder-Ultra-6.7B

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WaveCoder-Ultra-6.7B is an open-weights language model from Microsoft, released April 2024. Licence: mit.

Catalogue identity notes

Maker repo microsoft/wavecoder-ultra-6.7b @57049db: licence tag mit. Released 2024-04: month the maker's Hugging Face repo was created (createdAt 2024-04-11).

48.1±0.9#381 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of WaveCoder-Ultra-6.7B, out of 828 with a score, then more from Microsoft.

#modelscorewhereRAMreleased
454Gemini 1.5 Flash-8B (001) Google48±2comparison only2024-10
455T5 Large Google48±9local~4.6GB2019-10
456WaveCoder-Ultra-6.7B Microsoft48±1local~5.8GB2024-04
457Phi-3.5-MoE-instruct Microsoft48±1local~26GB2024-08
458Claude 3 Haiku Anthropic48±0comparison only2024-03
more from Microsoft
252Phi-4-reasoning-plus 14B Microsoft59±1local~10GB2025-04
261Phi-4-reasoning 14B Microsoft58±1api $0.22/M +local~10GB2025-04
365Phi-4 14B Microsoft53±1api $0.087/M +local~10GB2024-12

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #284 of 458 · 100% of its score · 3 tests

Beats Qwen3-VL 32B Instruct, Solar Open 100B (102B-A12B), GLM-4.7-Flash here, though they rank higher overall. Trails DeepSeek LLM 67B Chat, WizardLM 70B V1.0, Yi 1.5 9B Chat here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval75.0% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 70/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 50%sharp
HumanEval+69.5% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 69/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 41%sharp · at its level
MBPP+63.5% ✎▁···▁▁▂▅▇█▆▂ beats 45/75easy · avg 68%▇▇▇ 7.9%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 WaveCoder-Ultra-6.7B · needs ~4.1 GB · from huggingface.co/lmstudio-community
    chat in the terminal
    llama cli -hf lmstudio-community/wavecoder-ultra-6.7b-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf lmstudio-community/wavecoder-ultra-6.7b-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 WaveCoder-Ultra-6.7B · needs ~4.1 GB · from huggingface.co/lmstudio-community

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/lmstudio-community/wavecoder-ultra-6.7b-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 WaveCoder-Ultra-6.7B · needs ~4.1 GB · from huggingface.co/lmstudio-community
    chat in the terminal
    ollama run hf.co/lmstudio-community/wavecoder-ultra-6.7b-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~26.95 GB · from huggingface.co/microsoft (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='microsoft/wavecoder-ultra-6.7b', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/microsoft ↗

$ 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= 48.1

$ 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