whatmodel.sh / HuatuoGPT-o1-72B

HuatuoGPT-o1-72B

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HuatuoGPT-o1-72B is an open-weights language model from FreedomIntelligence (CUHK-SZ), released December 2024. Its published size is 72.71B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo FreedomIntelligence/HuatuoGPT-o1-72B @6048ce0: licence tag apache-2.0. 72,706,203,648 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-12: month the maker's Hugging Face repo was created (createdAt 2024-12-28).

58.7±4.3#243 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 HuatuoGPT-o1-72B, out of 1132 with a score, then more from FreedomIntelligence (CUHK-SZ).

#modelscorewhereRAMreleased
316EXAONE 4.0 32B LG AI Research59±1local~20GB2025-07
317Phi-4-reasoning-plus 14B Microsoft59±1local~10GB2025-04
318HuatuoGPT-o1-72B FreedomIntelligence (CUHK-SZ)59±4local~28GB2024-12
319Nemotron-Cascade-8B NVIDIA59±1local~6.3GB2025-12
320Qwen3 14B Alibaba59±1api $0.61/M +local~10GB2025-04
more from FreedomIntelligence (CUHK-SZ)
432HuatuoGPT-o1-70B FreedomIntelligence (CUHK-SZ)55±4local~22GB2024-12
522AceGPT-v2-8B-Chat FreedomIntelligence (CUHK-SZ)52±5local~6.6GB2024-06
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 · 92% of its score
testresultvs the others in its rundifficultycountswhy
BRIDGE clinical text, zero-shot average (87 tasks)41.0% ✎▁▁▁▁▁▂▂▃▄▆█▃ beats 81/97hard · avg 32%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 92%blunt · 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 HuatuoGPT-o1-72B · needs ~47.4 GB · from huggingface.co/bartowski
    chat in the terminal
    llama cli -hf bartowski/HuatuoGPT-o1-72B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf bartowski/HuatuoGPT-o1-72B-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 HuatuoGPT-o1-72B · needs ~47.4 GB · from huggingface.co/bartowski

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/bartowski/HuatuoGPT-o1-72B-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 HuatuoGPT-o1-72B · needs ~25.5 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/HuatuoGPT-o1-72B-GGUF:IQ2_XXS

MLX (Mac)

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run HuatuoGPT-o1-72B · needs ~40.9 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/HuatuoGPT-o1-72B-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/HuatuoGPT-o1-72B-4bit

Other ways to run it

  1. Run it as published · needs ~145.41 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/HuatuoGPT-o1-72B', 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 · BRIDGE Medical Leaderboard (H… 92%

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
BRIDGE Medical Leaderboard (Harvard Medical School / Brigham and Women's YLab)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 92%MIT
prior · pull toward the middle▇▇▇ 8%
weighted average= 58.7

$ 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