[whatmodel.sh](../../README.md) / Vicuna 33B v1.3 # Vicuna 33B v1.3 LMSYS · released 2023-06 · 33B params · non-commercial **score 41.3** ±1.1 · #542 of 732 · from 10 benchmark results across 8 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Vicuna 33B v1.3 among every model on that task or test, worst to best. 
task            vs the others         rank  counts  tests
chat & writing  ▁▁▁▃●▄▆█▆▇▇▃   #338 of 402     80%      6
chat & writing · #338 of 402 · 80% of its score Beats [OpenThinker3 7B](openthinker3-7b.md), [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md) here, though they rank higher overall. Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md), [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md) here, though they rank lower overall. 
test                                                      result  vs the others in its run   difficulty                counts  why
AlpacaEval 2 length-controlled                             17.6%  ▁▂▂▃▆●█▆▄▄▁▁ beats 44/122  medium · avg 35%             30%  sharp
Arena-Hard (v0.1, GPT-4-1106 judg…                          8.6%  ▂▁●▅█▅▄▃▃▅▃▁ beats 4/60    medium · avg 47%             16%  sharp
LMArena (text)                                            1173.0  ▁▁▁▂●▃▃▆▆▇█▃ beats 52/385  avg 1260                     12%  blunt
Polyrating rating (bias-aware re-…                        1105.0  ▂▁▂▁▃▇●▆▇▄▂▂ beats 49/95   avg 1187                     11%  typical
MT-Bench (GPT-4 single-answer gra…  7.1200 MT-Bench score (1-10)  ▆▂▂▆▆▄██●▄▄▆ beats 21/28   avg 8.539 MT-Bench scor…    8.7%  sharp · noisy
AlpacaEval 1.0 win rate (vs text-…                         89.0%  ▂▂·▆·▇▃▆●▆▄▂ beats 28/45   very easy · avg 94%         2.0%  sharp · noisy
other tests (4) 
test                                result  vs the others in its run    difficulty        counts  why
Factual knowledge recall, Premium…   34.3%  ▅····▃▅·██·● beats 11/13    medium · avg 52%    7.4%  at its level
EQ-Bench v1 (60 questions, paper …   36.5%  ▂··▄·▂●▄▂█·▂ beats 4/14     medium · avg 53%    5.2%  at its level
Factual knowledge recall, Premium…   44.0%  ▃▅▃··▅▃·█●▃▅ beats 10/13    medium · avg 61%    3.8%  at its level
Open PT-LLM Leaderboard average (…   65.3%  ▃▁▁▁▁▁▁▁▂●▅▁ beats 115/276  medium · avg 72%    2.8%  at its level · 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
AlpacaEval 1.0 and 2.0 leaderboards             2   32% ▇▇▇▇
Arena-Hard-Auto v0.1 leaderboard                1   16% ▇▇
LMArena leaderboard dataset                     1   12% ▇
Polyrating: A Cost-Effective and Bias-A…        1   11% ▇
Towards a Holistic Evaluation of LLMs o…        2   11% ▇
LMSYS Chatbot Arena leaderboard Space: …        1    9% ▇
EQ-Bench: An Emotional Intelligence Ben…        1    5% ▇
Open Portuguese LLM Leaderboard                 1    3% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 41.3
Licences: AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · 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/)) · Towards a Holistic Evaluation of LLMs on Factual Knowledge Recall ([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)) · EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models ([CC BY 4.0](../../licenses/paper-2312.06281v2-CC-BY-4.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) ## How to run it ### Ollama (recommended) 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Vicuna 33B v1.3 (Q4_K_M, needs ~20.3 GB, from [ollama.com](https://ollama.com/library/vicuna/tags)) ```sh ollama run vicuna:33b-q4_K_M ``` Other versions: Q8_0 (~40.1 GB) `ollama run vicuna:33b-q8_0` Model repository and documentation: [huggingface.co/lmsys](https://huggingface.co/lmsys/vicuna-33b-v1.3) [interactive version](https://whatmodel.sh/model/vicuna-33b-v1-3) · [JSON](https://whatmodel.sh/api?format=json&all=1)