[whatmodel.sh](../../README.md) / Vicuna 13B v1.3 # Vicuna 13B v1.3 LMSYS · released 2023-06 **score 37.1** ±2.0 · #609 of 732 · from 4 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Vicuna 13B v1.3 among every model on that task or test, worst to best.}

task            vs the others          rank  counts  tests
chat & writing  ▁▁▁●▆▄▆█▆▇▇▃   #368 of 402*     79%      2

chat & writing · #368 of 402 · 79% of its score

Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.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
AlpacaEval 2 length-controlled       10.8%  ▁▂▂▃●▆█▆▄▄▁▁ beats 26/122  medium · avg 35%        70%  sharp
AlpacaEval 1.0 win rate (vs text-…   82.1%  ▂▂·▆·▇▃●█▆▄▂ beats 21/45   very easy · avg 94%    8.9%  sharp · at its level · noisy

other tests (2)

test                                result  vs the others in its run  difficulty      counts  why
VEC embodied concepts, zero-shot …   55.2%  ▁····▁▁·▁·▃● beats 6/11   hard · avg 63%     19%  typical
VEC visual concepts, zero-shot pe…   63.5%  ▅▅▃●·▃··▃·▃▃ beats 5/11   hard · avg 66%    0.2%  blunt · at its level

## 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
AlpacaEval 1.0 and 2.0 leaderboards             2   79% ▇▇▇▇▇▇▇▇▇▇
Can Language Models Understand Physical…        2   19% ▇▇
prior: pull toward the middle                        2% ▇
weighted average                                   = 37.1

_{Licences: AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Can Language Models Understand Physical Concepts? ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-726-CC-BY-4-0.txt))} ## How to run it ### Other ways to run it 1. Run it as published (fp16, needs ~26.03 GB, from [huggingface.co/lmsys](https://huggingface.co/lmsys/vicuna-13b-v1.3/tree/6566e9cb1787585d1147dcf4f9bc48f29e1328d2)) ```sh uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='lmsys/vicuna-13b-v1.3', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])" ``` Model repository and documentation: [huggingface.co/lmsys](https://huggingface.co/lmsys/vicuna-13b-v1.3) [interactive version](https://whatmodel.sh/model/vicuna-13b-v1-3) · [JSON](https://whatmodel.sh/api?format=json&all=1)