whatmodel.sh / Snorkel-Mistral-PairRM-DPO

Snorkel-Mistral-PairRM-DPO

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Snorkel-Mistral-PairRM-DPO is an open-weights language model from Snorkel AI, released January 2024. Licence: apache-2.0.

Catalogue identity notes

Maker repo snorkelai/Snorkel-Mistral-PairRM-DPO @07af5d0: licence tag apache-2.0. Released 2024-01: month the maker's Hugging Face repo was created (createdAt 2024-01-19).

43.8±1.4#622 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 Snorkel-Mistral-PairRM-DPO, out of 1132 with a score.

#modelscorewhereRAMreleased
776Falcon3 10B Instruct TII44±3local~7.6GB2024-12
777Code Tulu 2 34B Allen Institute for AI44±2local~72GB2023-11
778Snorkel-Mistral-PairRM-DPO Snorkel AI44±1local~6.1GB2024-01
779Qwen1.5 MoE A2.7B Chat Alibaba44±6local~10GB2024-03
780Qwen3 1.7B Alibaba44±1local~2.6GB2025-04

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #345 of 466 * · 99% of its score · 2 tests

Beats Ministral 3 14B Reasoning 2512, Gemma 4 E4B, OpenThinker3 7B here, though they rank higher overall. Trails LFM2 350M, Flan-T5 XXL, LFM2 700M here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled26.4% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 70/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 59%typical
Arena-Hard (v0.1, GPT-4-1106 judge)20.7% ✎▂▁▁▅█▅▄▃▃▅▃▁ beats 15/60medium · avg 52%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 40%sharp · 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 Snorkel-Mistral-PairRM-DPO · needs ~4.4 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/Snorkel-Mistral-PairRM-DPO-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/Snorkel-Mistral-PairRM-DPO-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 Snorkel-Mistral-PairRM-DPO · 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/Snorkel-Mistral-PairRM-DPO-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 Snorkel-Mistral-PairRM-DPO · needs ~4.4 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/Snorkel-Mistral-PairRM-DPO-GGUF:Q4_K_M

Other ways to run it

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

Official weights and documentation: huggingface.co/snorkelai ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 59%, Arena-Hard-Auto v0.1 leaderbo… 40%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 59%Apache-2.0
Arena-Hard-Auto v0.1 leaderboard (repository README, pre-v2)1▇▇▇▇▇▇▇▇▇▇▇▇ 40%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 43.8

$ 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