whatmodel.sh / HuatuoGPT-o1-7B

HuatuoGPT-o1-7B

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HuatuoGPT-o1-7B is an open-weights language model from FreedomIntelligence (CUHK-SZ), released December 2024. Its published size is 7.62B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo FreedomIntelligence/HuatuoGPT-o1-7B @6487c80: licence tag apache-2.0. 7,615,616,512 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-12: month the maker's Hugging Face repo was created (createdAt 2024-12-26).

49.7±3.8#453 of 949 LLMsfrom 2 benchmark results across 2 sources* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of HuatuoGPT-o1-7B, out of 1132 with a score, then more from FreedomIntelligence (CUHK-SZ).

#modelscorewhereRAMreleased
582Molmo 72B (0924) Ai250±2local~309GB2024-09
583Codestral 22B v0.1 Mistral AI50±1local~15GB2024-05
584HuatuoGPT-o1-7B FreedomIntelligence (CUHK-SZ)50±4local~6GB2024-12
585Nous Hermes 2 Yi 34B Nous Research50±7local~22GB2023-12
586GLM-4 (0116) Z.ai50±1comparison only2024-01
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

other tests2 not in a task · 94% of its score
testresultvs the others in its rundifficultycountswhy
BRIDGE clinical text, zero-shot average (87 tasks)29.6% ✎▁▁▁▁▁▂▂▃▄▆█▃ beats 31/97hard · avg 32%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 64%blunt · at its level
SurgiQ surgical MCQ (zero-shot, log-probability scoring)57.9% ✎▂···▂▂·▃▂▃██ beats 10/17medium · avg 53%▇▇▇▇▇▇▇▇▇▇▇▇ 30%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-o1-7B · needs ~4.7 GB · from huggingface.co/bartowski
    chat in the terminal
    llama cli -hf bartowski/HuatuoGPT-o1-7B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf bartowski/HuatuoGPT-o1-7B-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-o1-7B · needs ~4.7 GB · from huggingface.co/bartowski

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/bartowski/HuatuoGPT-o1-7B-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 HuatuoGPT-o1-7B · needs ~4.7 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/HuatuoGPT-o1-7B-GGUF:Q4_K_M

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-o1-7B · needs ~4.3 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/HuatuoGPT-o1-7B-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/HuatuoGPT-o1-7B-4bit

Other ways to run it

  1. Run it as published · needs ~15.23 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-o1-7B', 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… 64%, SurgiQ: A Large-Scale Multi-D… 30%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 64%MIT
SurgiQ: A Large-Scale Multi-Domain Benchmark for Evaluating Surgical Understanding in Large Language Models (arXiv:2606.08071v1)1▇▇▇▇▇▇▇▇▇ 30%CC BY 4.0
prior · pull toward the middle▇▇ 6%
weighted average= 49.7

$ 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