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Minotaur 13B (fixed)

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Minotaur 13B (fixed) is an open-weights language model from OpenAccess AI Collective, released June 2023. Licence: apache-2.0.

Catalogue identity notes

Maker repo openaccess-ai-collective/minotaur-13b-fixed @5dac6f7: licence tag apache-2.0. Released 2023-06: month the maker's Hugging Face repo was created (createdAt 2023-06-12).

35.0±2.2#803 of 949 LLMsfrom 2 benchmark results across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Minotaur 13B (fixed), out of 1132 with a score.

#modelscorewhereRAMreleased
968Gemma 7B (base) Google35±1local~7.1GB2024-02
969Tulu 2 DPO 7B Allen Institute for AI35±1local~5.8GB2023-11
970Minotaur 13B (fixed) OpenAccess AI Collective35±2local~9.8GB2023-06
971Eurus-2-7B-SFT PRIME-RL35±3local~6.4GB2024-12
972Falcon 40B (base) TII35±1local~27GB2023-05

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #445 of 466 * · 98% of its score · 2 tests

Beats Phi-2 (2.7B) here, though they rank higher overall. Trails Gemma 3 270M IT, SmolLM2 360M Instruct, Granite 4.0 350M here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled11.5% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 27/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 77%typical
AlpacaEval 1.0 win rate (vs text-davinci-003, GPT-4 judge)66.0% ✎▂▂·▆·▇▃▆█▆▄▂ beats 13/45very easy · avg 96%▇▇▇▇▇▇▇▇ 20%sharp · noisy

$ 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 Minotaur 13B (fixed) · needs ~7.9 GB · from huggingface.co/TheBloke
    chat in the terminal
    llama cli -hf TheBloke/minotaur-13B-fixed-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf TheBloke/minotaur-13B-fixed-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 Minotaur 13B (fixed) · 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/minotaur-13B-fixed-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 Minotaur 13B (fixed) · needs ~7.9 GB · from huggingface.co/TheBloke
    chat in the terminal
    ollama run hf.co/TheBloke/minotaur-13B-fixed-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~26.03 GB · from huggingface.co/openaccess-ai-collective (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='openaccess-ai-collective/minotaur-13b-fixed', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/openaccess-ai-collective ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 98%

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
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 98%Apache-2.0
prior · pull toward the middle▇ 2%
weighted average= 35.0

$ 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