whatmodel.sh / AceGPT-v2-8B-Chat

AceGPT-v2-8B-Chat

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AceGPT-v2-8B-Chat is an open-weights language model from FreedomIntelligence (CUHK-SZ), released June 2024. Its published size is 8.03B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo FreedomIntelligence/AceGPT-v2-8B-Chat @562d099: licence tag apache-2.0. 8,030,261,248 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-06: month the maker's Hugging Face repo was created (createdAt 2024-06-20).

51.7±4.5#398 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 AceGPT-v2-8B-Chat, out of 1132 with a score, then more from FreedomIntelligence (CUHK-SZ).

#modelscorewhereRAMreleased
520Nemotron-4 340B Instruct NVIDIA52±2comparison only2024-06
521Aya Expanse 32B Cohere52±2api $0.75/M +local~22GB2024-10
522AceGPT-v2-8B-Chat FreedomIntelligence (CUHK-SZ)52±5local~6.6GB2024-06
523Reka Flash 3.1 (21B) Reka AI52±6local~14GB2025-05
524Gemma 3 12B Google52±1api $0.075/M +local~9.1GB2025-03
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
584HuatuoGPT-o1-7B FreedomIntelligence (CUHK-SZ)50±4local~6GB2024-12

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests1 not in a task · 91% of its score
testresultvs the others in its rundifficultycountswhy
AraGen-v3 3C3H score (Inception, Claude Sonnet 4 judge)35.0% ✎▁▁·▁▂▂▄█▄▅▄▄ beats 33/66hard · avg 31%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 91%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 AceGPT-v2-8B-Chat · needs ~4.9 GB · from huggingface.co/featherless-ai-quants
    chat in the terminal
    llama cli -hf featherless-ai-quants/FreedomIntelligence-AceGPT-v2-8B-Chat-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf featherless-ai-quants/FreedomIntelligence-AceGPT-v2-8B-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-v2-8B-Chat · needs ~4.9 GB · from huggingface.co/featherless-ai-quants

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/featherless-ai-quants/FreedomIntelligence-AceGPT-v2-8B-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-v2-8B-Chat · needs ~4.9 GB · from huggingface.co/featherless-ai-quants
    chat in the terminal
    ollama run hf.co/featherless-ai-quants/FreedomIntelligence-AceGPT-v2-8B-Chat-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~16.06 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-v2-8B-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… 91%

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)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 91%Apache-2.0
prior · pull toward the middle▇▇▇ 9%
weighted average= 51.7

$ 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