whatmodel.sh / OpenHermes-2.5-Code-290k-13B

OpenHermes-2.5-Code-290k-13B

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OpenHermes-2.5-Code-290k-13B is an open-weights language model from ajibawa-2023, released March 2024. Its published size is 13.02B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo ajibawa-2023/OpenHermes-2.5-Code-290k-13B @8dcd6ad: licence tag apache-2.0. 13,015,866,880 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-03: month the maker's Hugging Face repo was created (createdAt 2024-03-01).

40.5±0.8#526 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of OpenHermes-2.5-Code-290k-13B, out of 828 with a score.

#modelscorewhereRAMreleased
616Mixtral 8x7B Instruct v0.1 Mistral41±1local~29GB2023-12
617Molmo 7B-D (0924) Ai241±1local~35GB2024-09
618OpenHermes-2.5-Code-290k-13B ajibawa-202341±1local~9.8GB2024-03
619Granite 4.0 H 1B IBM40±1local~2.3GB2025-10
620Llama3-OpenBioLLM-8B Saama AI Labs40±2local~6.6GB2024-04

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #364 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 Qwen2.5 1.5B Instruct, Phi-3-mini-128k-instruct (Apr 2024), OpenHermes 2.5 Mistral 7B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval54.3% ✎▁·▁▁▃▄▆▅█▃▁▁ beats 39/99easy · avg 79%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 54%sharp · at its level
HumanEval+48.8% ✎▁·▁▁▁▃▄▄▄█▃▁ beats 39/99easy · avg 74%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 39%sharp · at its level
MBPP+45.8% ✎▁···▁▁▂▅▇█▆▂ beats 11/75easy · 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 OpenHermes-2.5-Code-290k-13B · needs ~7.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/OpenHermes-2.5-Code-290k-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 OpenHermes-2.5-Code-290k-13B · needs ~7.9 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/OpenHermes-2.5-Code-290k-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 OpenHermes-2.5-Code-290k-13B · needs ~7.9 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/OpenHermes-2.5-Code-290k-13B-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~26.03 GB · from huggingface.co/ajibawa-2023 (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='ajibawa-2023/OpenHermes-2.5-Code-290k-13B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/ajibawa-2023 ↗

$ 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= 40.5

$ 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