whatmodel.sh / Orca 2 13B

Orca 2 13B

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Orca 2 13B is an open-weights language model from Microsoft, released November 2023. Licence: other (microsoft-research-license).

Catalogue identity notes

Maker repo microsoft/Orca-2-13b @2539ff5: licence tag other (microsoft-research-license). Released 2023-11: month the maker's Hugging Face repo was created (createdAt 2023-11-14).

32.4±2.4#624 of 719 LLMsfrom 5 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Orca 2 13B, out of 828 with a score, then more from Microsoft.

#modelscorewhereRAMreleased
719OLMo 2 1B Instruct (0425) Allen Institute for AI33±2local~2.4GB2025-04
720DeepSeek-Coder 1.3B Base DeepSeek32±1local~2.4GB2023-10
721Orca 2 13B Microsoft32±2local~9.8GB2023-11
722Gemma 7B IT Google32±1local~7.1GB2024-02
723Mistral 7B Instruct v0.1 Mistral32±1local~6.1GB2023-09
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

other tests5 not in a task · 97% of its score
testresultvs the others in its rundifficultycountswhy
Natural Questions (closed book)17.8% ✎▁·▁·▁▂▁▂·▅▂█ beats 15/28medium · avg 58%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 72%typical
TriviaQA9.8% ✎▁··▁█▃▃▁▄▅▄█ beats 6/28easy · avg 79%▇▇▇▇ 10%at its level · noisy
Vectara hallucination leaderboard, 2025 dataset (HHEM-2.3, factual consistency rate)97.5% ✎▁▁▂▃▄▄█▆▃▂▂▁ beats 114/140very easy · avg 94%▇▇▇ 6.5%blunt · noisy
HaluEval QA hallucination detection (accuracy)66.2% ✎█▁▁·▁▁·▁▁▁▁▃ beats 26/27medium · avg 70%▇▇ 4.5%sharp · too hard for it · noisy
TruthfulQA MC255.8% ✎▅▃█▆▁▂▅···▁▁ beats 27/31medium · avg 64%▇▇ 4.4%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 Orca 2 13B · needs ~7.9 GB · from huggingface.co/TheBloke
    chat in the terminal
    llama cli -hf TheBloke/Orca-2-13B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf TheBloke/Orca-2-13B-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 Orca 2 13B · 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/Orca-2-13B-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 Orca 2 13B · needs ~7.9 GB · from huggingface.co/TheBloke
    chat in the terminal
    ollama run hf.co/TheBloke/Orca-2-13B-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~52.06 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/Orca-2-13b', 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 · Hallucinations Leaderboard re… 91%, Vectara hallucination leaderb… 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
Hallucinations Leaderboard results (HF dataset hallucinations-leaderboard/results)4▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 91%Apache-2.0
Vectara hallucination leaderboard (vectara/hallucination-leaderboard repository)1▇▇ 6%Apache-2.0
prior · pull toward the middle▇ 3%
weighted average= 32.4

$ 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