whatmodel.sh / GPT4All-13B-Snoozy

GPT4All-13B-Snoozy

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GPT4All-13B-Snoozy is an open-weights language model from Nomic AI, released April 2023. Its published size is 13B parameters. Licence: gpl.

Catalogue identity notes

Comparison-only. Arena entry 'gpt4all-13b-snoozy' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'gpt4all-13b-snoozy' as 'GPT4All-13B-Snoozy' (Nomic AI, Non-commercial), linking https://huggingface.co/nomic-ai/gpt4all-13b-snoozy. Maker repo nomic-ai/gpt4all-13b-snoozy @8e4f7bb: licence tag gpl. Parameters: nominal (card: 'A finetuned LLama 13B model'). Released 2023-04: month the maker's Hugging Face repo was created (createdAt 2023-04-24).

26.3±2.0#667 of 719 LLMsfrom 3 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of GPT4All-13B-Snoozy, out of 828 with a score.

#modelscorewhereRAMreleased
768Qwen2.5 0.5B Instruct Alibaba27±2local~1.8GB2024-09
769GPT-3 text-davinci-001 OpenAI26±3comparison only2022-01
770GPT4All-13B-Snoozy Nomic AI26±2local~56GB2023-04
771OPT-13B (13 billion parameters) Meta26±1local~29GB2022-05
772Granite 4.0 350M IBM26±1local~1.7GB2025-10

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▃▃▅▄▅█▆▆▇▃ #383 of 393 · 98% of its score · 3 tests

Beats Phi-2 (2.7B), Falcon 40B Instruct, ChatGLM3-6B here, though they rank higher overall. Trails Gemma 3 270M IT, SmolLM2 360M Instruct, Granite 4.0 350M here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024)924.0 ✎▂▁▃▁▃▇█▆▇▄▂▂ beats 6/95avg 1201▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 34%blunt
LMArena (text)1067.6 ✎▁▁▃▂▃▃▃▆▆▇█▃ beats 10/379avg 1268▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 34%blunt
MT-Bench (GPT-4 single-answer grading, LMSYS runs)5.4100 MT-Bench score (1-10) ✎▆▂▂▆▆▄██▂▄▄▆ beats 11/28avg 8.819 MT-Bench score (1-10)▇▇▇▇▇▇▇▇▇▇▇▇ 30%sharp · noisy

$ where to run it

Other ways to run it

  1. Run it as published · needs ~52.06 GB · from huggingface.co/nomic-ai (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='nomic-ai/gpt4all-13b-snoozy', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/nomic-ai ↗

$ how the score was worked out · Polyrating: A Cost-Effective… 34%, LMArena leaderboard dataset 34%, LMSYS Chatbot Arena leaderboa… 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
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3)1▇▇▇▇▇▇▇▇▇▇ 34%CC-BY-4.0
LMArena leaderboard dataset1▇▇▇▇▇▇▇▇▇▇ 34%CC BY 4.0
LMSYS Chatbot Arena leaderboard Space: MT-Bench column of the published leaderboard table1▇▇▇▇▇▇▇▇▇ 30%Apache-2.0
prior · pull toward the middle▇ 2%
weighted average= 26.3

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data updated · sources and licences · json