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AFM-4.5B

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AFM-4.5B is an open-weights language model from Arcee AI, released July 2025. Its published size is 4.62B parameters and a 66k-token context window. Licence: apache-2.0.

Catalogue identity notes

arcee-ai/AFM-4.5B @e84d197 (created 2025-07-29, licence apache-2.0). Arcee Foundation Model 4.5B instruct (July 2025). Independent rows: EuroEval English (both datasets).

Model information sources

Release date ↗ · Weights licence ↗

HF safetensors metadata 4,619,189,760 parameters.

49.0±5.7#470 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 AFM-4.5B, out of 1132 with a score, then more from Arcee AI.

#modelscorewhereRAMreleased
601Hammer2.1-1.5b MadeAgents49±4local~2.4GB2024-12
602Artigenz-Coder-DS-6.7B Artigenz49±1local~5.8GB2024-04
603AFM-4.5B Arcee AI49±6local~4.6GB2025-07
604LFM2.5 1.2B Thinking Liquid AI49±2local~2.2GB2026-01
605Qwen3-VL 2B Thinking Alibaba49±1local~2.7GB2025-10
more from Arcee AI
237Trinity-Large-Thinking Arcee AI63±2api $0.39/M +local~141GB2026-04
312Trinity-Large-Preview Arcee AI59±2local~155GB2026-01
491Trinity Mini (26B-A3B) Arcee AI53±6local~18GB2025-12

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests2 not in a task · 86% of its score
testresultvs the others in its rundifficultycountswhy
EuroEval HellaSwag (English), test split, 5-shot generative MC, accuracy60.2% ✎▃·▁▁▂▁▂▅█▇▄▁ beats 34/100medium · avg 68%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 51%at its level · noisy
EuroEval Life in the UK (English knowledge MC), test split, 5-shot, accuracy86.5% ✎▁▁▁▁▁▁▁▂▄█▄▃ beats 102/146easy · avg 83%▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 34%noisy

$ where to run it

llama.cpp · recommended

  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 AFM-4.5B · needs ~2.92 GB · from huggingface.co/arcee-ai (official)
    chat in the terminal
    llama cli -hf arcee-ai/AFM-4.5B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf arcee-ai/AFM-4.5B-GGUF:Q4_K_M

LM Studio

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run AFM-4.5B · needs ~2.92 GB · from huggingface.co/arcee-ai (official)

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/arcee-ai/AFM-4.5B-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run AFM-4.5B · needs ~2.92 GB · from huggingface.co/arcee-ai (official)
    chat in the terminal
    ollama run hf.co/arcee-ai/AFM-4.5B-GGUF:Q4_K_M

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

$ how the score was worked out · EuroEval leaderboards (result… 86%

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
EuroEval leaderboards (results collection), English generative leaderboard2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 86%MIT
prior · pull toward the middle▇▇▇▇ 14%
weighted average= 49.0

$ improve this page

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data updated · sources and licences · json