[whatmodel.sh](../../README.md) / FastChat-T5 3B # FastChat-T5 3B LMSYS · released 2023-04 · 3B params · apache-2.0 **score 21.1** ±1.9 · #698 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 FastChat-T5 3B among every model on that task or test, worst to best.}

task            vs the others         rank  counts  tests
chat & writing  ●▁▁▃▆▄▆█▆▇▇▃   #401 of 402     98%      3

chat & writing · #401 of 402 · 98% of its score

Trails [Gemma 3 270M IT](gemma-3-270m.md) here, though they rank lower overall.

test                                                      result  vs the others in its run  difficulty                counts  why
LMArena (text)                                             992.6  ●▁▁▂▃▃▃▆▆▇█▃ beats 3/385  avg 1260                     37%  blunt
Polyrating rating (bias-aware re-…                         879.0  ●▁▂▁▃▇█▆▇▄▂▂ beats 4/95   avg 1187                     34%  typical
MT-Bench (GPT-4 single-answer gra…  3.0400 MT-Bench score (1-10)  ●▂▂▆▆▄██▂▄▄▆ beats 2/28   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                                   = 21.1

_{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 (fp16, needs ~6.71 GB, NVIDIA only, from [github.com/lm-sys](https://github.com/lm-sys/FastChat/blob/587d5cfa1609a43d192cedb8441cac3c17db105d/README.md)) ```sh pip3 install "fschat[model_worker,webui]" && python3 -m fastchat.serve.cli --model-path lmsys/fastchat-t5-3b-v1.0 ``` Other versions: fp16 (~6.71 GB, Mac only) `pip3 install "fschat[model_worker,webui]" && python3 -m fastchat.serve.cli --model-path lmsys/fastchat-t5-3b-v1.0 --device mps` Model repository and documentation: [huggingface.co/lmsys](https://huggingface.co/lmsys/fastchat-t5-3b-v1.0) [interactive version](https://whatmodel.sh/model/fastchat-t5-3b) · [JSON](https://whatmodel.sh/api?format=json&all=1)