whatmodel.sh / Contextual KTO Mistral PairRM

Contextual KTO Mistral PairRM

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Contextual KTO Mistral PairRM is an open-weights language model from Contextual AI, released March 2024. Its published size is 7.24B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo ContextualAI/Contextual_KTO_Mistral_PairRM @98bee13: licence tag apache-2.0. 7,241,732,096 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-03: month the maker's Hugging Face repo was created (createdAt 2024-03-05).

46.4±1.7#544 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 Contextual KTO Mistral PairRM, out of 1132 with a score.

#modelscorewhereRAMreleased
686Mistral Large (2402) Mistral46±1comparison only2024-02
687Jamba 1.5 Mini AI21 Labs46±1local~34GB2024-08
688Contextual KTO Mistral PairRM Contextual AI46±2local~6.1GB2024-03
689MedGemma 4B IT Google46±3local~4.1GB2025-05
690OLMo 2 13B Instruct (1124) Allen Institute for AI46±1local~9.6GB2024-12

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #321 of 466 * · 99% of its score · 1 test

Beats Ministral 3 14B Reasoning 2512, Gemma 4 E4B, OpenThinker3 7B 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-controlled29.7% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 77/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%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 Contextual KTO Mistral PairRM · needs ~4.4 GB · from huggingface.co/featherless-ai-quants
    chat in the terminal
    llama cli -hf featherless-ai-quants/ContextualAI-Contextual_KTO_Mistral_PairRM-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/ContextualAI-Contextual_KTO_Mistral_PairRM-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 Contextual KTO Mistral PairRM · needs ~4.4 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/ContextualAI-Contextual_KTO_Mistral_PairRM-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 Contextual KTO Mistral PairRM · needs ~4.4 GB · from huggingface.co/featherless-ai-quants
    chat in the terminal
    ollama run hf.co/featherless-ai-quants/ContextualAI-Contextual_KTO_Mistral_PairRM-GGUF:Q4_K_M

Other ways to run it

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

Official weights and documentation: huggingface.co/ContextualAI ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 99%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 46.4

$ 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