whatmodel.sh / Mistral7B-PairRM-SPPO-ExPO

Mistral7B-PairRM-SPPO-ExPO

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Mistral7B-PairRM-SPPO-ExPO is an open-weights language model from Chujie Zheng (ExPO), released May 2024. Its published size is 7.24B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo chujiezheng/Mistral7B-PairRM-SPPO-ExPO @90d1dc0: licence tag apache-2.0. 7,241,732,096 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-05: month the maker's Hugging Face repo was created (createdAt 2024-05-04).

47.1±1.6#519 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 Mistral7B-PairRM-SPPO-ExPO, out of 1132 with a score, then more from Chujie Zheng (ExPO).

#modelscorewhereRAMreleased
658Infinity-Instruct-3M-0613-Llama3-70B BAAI47±2local~29GB2024-06
659Hunyuan-Standard-Vision (2024-12-31) Tencent47±4comparison only2024-12
660Mistral7B-PairRM-SPPO-ExPO Chujie Zheng (ExPO)47±2local~6.1GB2024-05
661Stable LM 2 12B Chat Stability AI47±6local~8.9GB2024-04
662Phi-4-mini-reasoning 3.8B Microsoft47±2api $0.13/M +local~3.8GB2025-04
more from Chujie Zheng (ExPO)
548Llama-3-Instruct-8B-SimPO-ExPO Chujie Zheng (ExPO)51±1local~6.6GB2024-05
730internlm2-chat-20b-ExPO Chujie Zheng (ExPO)45±2local~14GB2024-05
750tulu-2-dpo-70b-ExPO Chujie Zheng (ExPO)45±2local~28GB2024-04

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #304 of 466 * · 93% of its score · 1 test

Beats Ministral 3 14B Reasoning 2512, Gemma 4 E4B, Ministral 3 8B here, though they rank higher overall. Trails Flan-T5 XXL, LFM2 700M, EXAONE 3.5 2.4B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled31.8% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 84/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%at its level
other tests1 not in a task · 5.4% of its score
testresultvs the others in its rundifficultycountswhy
Open PT-LLM Leaderboard average (9 Portuguese tasks)63.0% ✎▃▁▁▁▁▁▁▁▃█▅▁ beats 99/276medium · avg 73%▇▇ 5.4%at its level · noisy

$ 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 Mistral7B-PairRM-SPPO-ExPO · needs ~4.4 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/Mistral7B-PairRM-SPPO-ExPO-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/Mistral7B-PairRM-SPPO-ExPO-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 Mistral7B-PairRM-SPPO-ExPO · needs ~4.4 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/Mistral7B-PairRM-SPPO-ExPO-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 Mistral7B-PairRM-SPPO-ExPO · needs ~4.4 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/Mistral7B-PairRM-SPPO-ExPO-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~14.48 GB · from huggingface.co/chujiezheng (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='chujiezheng/Mistral7B-PairRM-SPPO-ExPO', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/chujiezheng ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 93%, Open Portuguese LLM Leaderboa… 5%

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
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇▇ 5%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 47.1

$ 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