whatmodel.sh / OpenELM 3B Instruct

OpenELM 3B Instruct

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OpenELM 3B Instruct is an open-weights language model from Apple, released April 2024. Its published size is 3.04B parameters. Licence: apple-amlr.

Catalogue identity notes

Maker repo apple/OpenELM-3B-Instruct @8288250: licence tag apple-amlr. 3,036,647,424 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-04: month the maker's Hugging Face repo was created (createdAt 2024-04-12). Comparison-only here: no run command was verified for this checkpoint (custom OpenELM code that loads a separate Llama-2 tokenizer; see the model card).

23.6±7.3#905 of 949 LLMsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of OpenELM 3B Instruct, out of 1132 with a score.

#modelscorewhereRAMreleased
1083StableLM-Base-Alpha 3B v2 Stability AI24±1local~13GB2023-08
1084Qwen1.5 1.8B Chat Alibaba24±2local~2.7GB2024-01
1085OpenELM 3B Instruct Apple24±7local~3.5GB2024-04
1086Pythia 6.9B EleutherAI23±1local~6.1GB2023-02
1087OpenAssistant SFT-4 Pythia 12B LAION (OpenAssistant)22±1local~9.5GB2023-04

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests1 not in a task · 76% of its score
testresultvs the others in its rundifficultycountswhy
Vectara hallucination leaderboard, 2025 dataset (HHEM-2.3, factual consistency rate)75.2% ✎▃▁▂▃▄▄█▆▃▂▂▁ beats 3/140very easy · avg 94%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 76%blunt · 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 OpenELM 3B Instruct · needs ~1.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/OpenELM-3B-Instruct-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/OpenELM-3B-Instruct-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 OpenELM 3B Instruct · needs ~1.9 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/OpenELM-3B-Instruct-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 OpenELM 3B Instruct · needs ~1.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/OpenELM-3B-Instruct-GGUF:Q4_K_M

Official weights and documentation: huggingface.co/apple ↗

$ how the score was worked out · Vectara hallucination leaderb… 76%

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
Vectara hallucination leaderboard (vectara/hallucination-leaderboard repository)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 76%Apache-2.0
prior · pull toward the middle▇▇▇▇▇▇▇ 24%
weighted average= 23.6

$ 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