[whatmodel.sh](../../README.md) / Vicuna 13B v1.5 # Vicuna 13B v1.5 LMSYS · released 2023-08 · 13B params · llama2 **score 38.3** ±1.4 · #591 of 732 · from 5 benchmark results across 5 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 v1.5 among every model on that task or test, worst to best.}

task            vs the others         rank  counts  tests
chat & writing  ▁▁▁●▆▄▆█▆▇▇▃   #364 of 402     95%      4

chat & writing · #364 of 402 · 95% of its score

Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [Code Llama 34B Instruct](codellama-34b-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 H 350M](granite-4-0-h-350m.md) here, though they rank lower overall.

test                                                      result  vs the others in its run   difficulty                counts  why
AlpacaEval 2 length-controlled                             10.5%  ▁▂▂▃●▆█▆▄▄▁▁ beats 24/122  medium · avg 35%             40%  sharp
LMArena (text)                                            1141.6  ▁▁▁●▃▃▃▆▆▇█▃ beats 34/385  avg 1260                     21%  blunt
Polyrating rating (bias-aware re-…                        1077.0  ▂▁▂▁▃▇●▆▇▄▂▂ beats 36/95   avg 1187                     19%  typical
MT-Bench (GPT-4 single-answer gra…  6.5700 MT-Bench score (1-10)  ▆▂▂▆▆▄█●▂▄▄▆ beats 18/28   avg 8.539 MT-Bench scor…     15%  sharp · noisy

other tests (1)

test                                result  vs the others in its run    difficulty        counts  why
Open PT-LLM Leaderboard average (…   63.7%  ▃▁▁▁▁▁▁▁●█▅▁ beats 104/276  medium · avg 72%    4.5%  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             1   40% ▇▇▇▇▇
LMArena leaderboard dataset                     1   21% ▇▇▇
Polyrating: A Cost-Effective and Bias-A…        1   19% ▇▇
LMSYS Chatbot Arena leaderboard Space: …        1   15% ▇▇
Open Portuguese LLM Leaderboard                 1    4% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 38.3

_{Licences: AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · 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)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))} ## How to run it ### llama.cpp (recommended) 1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) ```sh # macOS / Linux curl -LsSf https://llama.app/install.sh | sh # Windows irm https://llama.app/install.ps1 | iex ``` 2. Run Vicuna 13B v1.5 (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF/tree/f1e0f2785bc042bac761d15584ebfb40c57221c4)) ```sh llama cli -hf TheBloke/vicuna-13B-v1.5-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf TheBloke/vicuna-13B-v1.5-GGUF:Q4_K_M ``` Other versions: Q8_0 (~13.8 GB) `llama cli -hf TheBloke/vicuna-13B-v1.5-GGUF:Q8_0` ### LM Studio 1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download) 2. Run Vicuna 13B v1.5 (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF/tree/f1e0f2785bc042bac761d15584ebfb40c57221c4)) ```sh lms get https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: Q8_0 (~13.8 GB) `lms get https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF@Q8_0 -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Vicuna 13B v1.5 (Q4_K_M, needs ~8.3 GB, from [ollama.com](https://ollama.com/library/vicuna/tags)) ```sh ollama run vicuna:13b-v1.5-q4_K_M ``` Other versions: Q8_0 (~16.1 GB) `ollama run vicuna:13b-v1.5-q8_0` Model repository and documentation: [huggingface.co/lmsys](https://huggingface.co/lmsys/vicuna-13b-v1.5) [interactive version](https://whatmodel.sh/model/vicuna-13b-v1-5) · [JSON](https://whatmodel.sh/api?format=json&all=1)