whatmodel.sh / AceGPT-7B-chat

AceGPT-7B-chat

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AceGPT-7B-chat is an open-weights language model from FreedomIntelligence (CUHK-SZ), released September 2023. Licence: apache-2.0.

Catalogue identity notes

Maker repo FreedomIntelligence/AceGPT-7B-chat @252565c: licence tag apache-2.0. Released 2023-09: month the maker's Hugging Face repo was created (createdAt 2023-09-21).

39.9±3.5#713 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 AceGPT-7B-chat, out of 1132 with a score, then more from FreedomIntelligence (CUHK-SZ).

#modelscorewhereRAMreleased
873Code Llama 34B Instruct Meta40±1local~22GB2023-08
874Code Llama 34B (base) Meta40±1local~23GB2023-08
875AceGPT-7B-chat FreedomIntelligence (CUHK-SZ)40±4local~5.8GB2023-09
876Starling-LM 7B beta Nexusflow40±1local~6GB2024-03
877LFM2 1.2B Liquid AI40±2local~2.2GB2025-07
more from FreedomIntelligence (CUHK-SZ)
318HuatuoGPT-o1-72B FreedomIntelligence (CUHK-SZ)59±4local~28GB2024-12
432HuatuoGPT-o1-70B FreedomIntelligence (CUHK-SZ)55±4local~22GB2024-12
522AceGPT-v2-8B-Chat FreedomIntelligence (CUHK-SZ)52±5local~6.6GB2024-06

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #350 of 466 * · 65% of its score · 1 test

Beats Ministral 3 14B Reasoning 2512, Gemma 4 E4B, OpenThinker3 7B here, though they rank higher overall. Trails LFM2 350M, Gemma 3 1B, Flan-T5 XXL here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arabic IFEval prompt-level (Inception)22.0% ✎▁·▁▁▂▁▄▄▆▄█▄ beats 11/38medium · avg 49%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 65%sharp
other tests1 not in a task · 29% of its score
testresultvs the others in its rundifficultycountswhy
AraGen-v3 3C3H score (Inception, Claude Sonnet 4 judge)12.4% ✎▁▁·▁▂▃▄█▄▅▄▄ beats 8/66hard · avg 31%▇▇▇▇▇▇▇▇▇▇▇▇ 29%typical

$ 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 AceGPT-7B-chat · needs ~4.1 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/AceGPT-7B-chat-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/AceGPT-7B-chat-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 AceGPT-7B-chat · needs ~4.1 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/AceGPT-7B-chat-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 AceGPT-7B-chat · needs ~4.1 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/AceGPT-7B-chat-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~22.47 GB · from huggingface.co/FreedomIntelligence (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='FreedomIntelligence/AceGPT-7B-chat', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/FreedomIntelligence ↗

$ how the score was worked out · Inception Arabic/Hindi leader… 95%

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
Inception Arabic/Hindi leaderboards (AraGen-v3, HindiGen-v1, Arabic IFEval)2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 95%Apache-2.0
prior · pull toward the middle▇▇ 5%
weighted average= 39.9

$ 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