[whatmodel.sh](../../README.md) / GPT4All-13B-Snoozy # GPT4All-13B-Snoozy Nomic AI · released 2023-04 · 13B params · gpl **score 29.2** ±1.9 · #670 of 732 · from 3 benchmark results across 3 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks GPT4All-13B-Snoozy among every model on that task or test, worst to best.}

task            vs the others         rank  counts  tests
chat & writing  ▁▁●▃▆▄▆█▆▇▇▃   #392 of 402     98%      3

chat & writing · #392 of 402 · 98% of its score

Beats [Phi-2 (2.7B)](phi-2.md), [Falcon 40B Instruct](falcon-40b-instruct.md), [ChatGLM3-6B](chatglm3-6b.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Granite 4.0 350M](granite-4-0-350m.md) here, though they rank lower overall.

test                                                      result  vs the others in its run   difficulty                counts  why
LMArena (text)                                            1067.8  ▁▁●▂▃▃▃▆▆▇█▃ beats 10/385  avg 1260                     37%  blunt
Polyrating rating (bias-aware re-…                         924.0  ▂▁●▁▃▇█▆▇▄▂▂ beats 6/95    avg 1187                     34%  typical
MT-Bench (GPT-4 single-answer gra…  5.4100 MT-Bench score (1-10)  ▆▂▂▆▆●██▂▄▄▆ beats 11/28   avg 8.539 MT-Bench scor…     27%  sharp · noisy

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
LMArena leaderboard dataset                     1   37% ▇▇▇▇
Polyrating: A Cost-Effective and Bias-A…        1   34% ▇▇▇▇
LMSYS Chatbot Arena leaderboard Space: …        1   27% ▇▇▇
prior: pull toward the middle                        2% ▇
weighted average                                   = 29.2

_{Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · LMSYS Chatbot Arena leaderboard Space: MT-Bench column of the published leaderboard table ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))} ## How to run it ### Other ways to run it 1. Run it as published (fp32, needs ~52.06 GB, from [huggingface.co/nomic-ai](https://huggingface.co/nomic-ai/gpt4all-13b-snoozy/tree/8e4f7bb712d579c820fdeb75ba81a8c48ad96b1e)) ```sh 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'])" ``` Model repository and documentation: [huggingface.co/nomic-ai](https://huggingface.co/nomic-ai/gpt4all-13b-snoozy) [interactive version](https://whatmodel.sh/model/gpt4all-13b-snoozy) · [JSON](https://whatmodel.sh/api?format=json&all=1)