whatmodel.sh / Aya 23 35B

Aya 23 35B

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Aya 23 35B is an open-weights language model from Cohere For AI, released May 2024. Its published size is 34.98B parameters and a 8k-token context window. Licence: cc-by-nc-4.0.

Catalogue identity notes

Maker repo CohereLabs/aya-23-35B @ce62acc: licence tag cc-by-nc-4.0. 34,980,831,232 parameters from the Hugging Face safetensors metadata. Dense, so active = total. Released 2024-05: month the maker's Hugging Face repo was created (createdAt 2024-05-19). The repo was previously published as CohereForAI/aya-23-35B (the Hugging Face API redirects that id here); leaderboard rows may use the old id. Ollama tags aya:35b-23-q4_K_M, aya:35b-23-q8_0 are the library's instruct/chat builds of this checkpoint (tag names on the pinned tags page); download sizes are estimated from the parameter count.

49.2±2.7#358 of 719 LLMsfrom 5 benchmark results across 4 sources

$ how it compares

The LLMs ranked either side of Aya 23 35B, out of 828 with a score, then more from Cohere For AI.

#modelscorewhereRAMreleased
427OLMo 2 32B Instruct (0325) Ai249±1local~21GB2025-03
428OpenThinker2 7B Open Thoughts49±2local~6.4GB2025-04
429Aya 23 35B Cohere For AI49±3local~22GB2024-05
430Granite 4.1 3B IBM49±1local~3.7GB2026-04
431OpenCodeInterpreter-DS-6.7B M-A-P49±1local~5.8GB2024-02
more from Cohere For AI
582Aya 23 8B Cohere For AI43±3local~6.2GB2024-05

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
Polish EQ-Bench v2 (SpeakLeash)58.4% ✎▁··▁▁▂▂▆▅▆█▆ beats 37/70medium · avg 64%▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 36%at its level
Open Persian LLM Leaderboard v2 average (21 tasks)55.2% ✎▂▄▂▃█▄▃▆▃▅▂▂ beats 43/81medium · avg 58%▇▇▇▇▇▇▇▇▇▇▇ 28%blunt · at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)71.4% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 183/276medium · avg 73%▇▇▇▇▇▇ 14%at its level · noisy
EuroEval HellaSwag (English), test split, 5-shot generative MC, accuracy83.5% ✎▃·▁▁▂▁▂▅█▇▄▁ beats 80/100medium · avg 68%▇▇▇▇▇ 11%at its level · noisy
EuroEval Life in the UK (English knowledge MC), test split, 5-shot, accuracy81.8% ✎▁▁▁▁▁▁▁▂▄█▄▃ beats 75/146easy · avg 83%▇▇▇ 7.5%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 Aya 23 35B · needs ~21.5 GB · from huggingface.co/lmstudio-community
    chat in the terminal
    llama cli -hf lmstudio-community/aya-23-35B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf lmstudio-community/aya-23-35B-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 Aya 23 35B · needs ~21.5 GB · from huggingface.co/lmstudio-community

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/lmstudio-community/aya-23-35B-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 Aya 23 35B · needs ~21.49 GB · from ollama.com
    chat in the terminal
    ollama run aya:35b-23-q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Aya 23 35B · needs ~19.7 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/aya-23-35B-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/aya-23-35B-4bit

Official weights and documentation: huggingface.co/CohereLabs ↗

$ how the score was worked out · SpeakLeash Polish EQ-Bench le… 36%, Open Persian LLM Leaderboard… 28%, EuroEval leaderboards (result… 19% and 1 more

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
SpeakLeash Polish EQ-Bench leaderboard1▇▇▇▇▇▇▇▇▇▇▇ 36%Apache-2.0
Open Persian LLM Leaderboard v2 (Part DP AI and AUT NLP Lab)1▇▇▇▇▇▇▇▇ 28%Apache-2.0
EuroEval leaderboards (results collection), English generative leaderboard2▇▇▇▇▇▇ 19%MIT
Open Portuguese LLM Leaderboard (archived)1▇▇▇▇ 14%Apache-2.0
prior · pull toward the middle▇ 3%
weighted average= 49.2

$ 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