[whatmodel.sh](../../README.md) / Vicuna 7B v1.5
# Vicuna 7B v1.5
LMSYS · released 2023-08 · 7B params · llama2
**score 35.0** ±1.1 · #627 of 732 · from 10 benchmark results across 7 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Vicuna 7B v1.5 among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁●▆▄▆█▆▇▇▃ #379 of 402 55% 4
chat & writing · #379 of 402 · 55% of its score
Beats [Phi-2 (2.7B)](phi-2.md), [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md), [Nous-Hermes-13b](nous-hermes-13b.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 7.6% ▁▂▂●▆▆█▆▄▄▁▁ beats 16/122 medium · avg 35% 19% sharp
LMArena (text) 1115.4 ▁▁▁●▃▃▃▆▆▇█▃ beats 20/385 avg 1260 14% blunt
Polyrating rating (bias-aware re-… 1058.0 ▂▁▂▁▃●█▆▇▄▂▂ beats 29/95 avg 1187 12% typical
MT-Bench (GPT-4 single-answer gra… 6.1700 MT-Bench score (1-10) ▆▂▂▆▆▄●█▂▄▄▆ beats 13/28 avg 8.539 MT-Bench scor… 10% sharp · noisy
other tests (6)
test result vs the others in its run difficulty counts why
LiveBench 2024-07-26 12.6% ▁▁▁●▂▃▂█▆▃▅▃ beats 7/61 medium · avg 38% 28% typical
Natural Questions (closed book) 20.6% ▁·▁·▁▂▁▂·▅●█ beats 16/28 medium · avg 48% 12% typical
Open PT-LLM Leaderboard average (… 57.0% ▃▁▁▁▁▁▁▁●█▅▁ beats 82/276 medium · avg 72% 2.6% at its level · noisy
TriviaQA 56.4% ▁··▁█▃▃▁▄▅●█ beats 21/28 easy · avg 73% 1.6% at its level · noisy
HaluEval QA hallucination detecti… 38.1% ●▁▁·▁▁·▁▁▁▁▁ beats 6/27 hard · avg 66% 0.9% noisy
TruthfulQA MC2 50.4% ▅▃█▆▁●▅···▁▁ beats 23/31 medium · avg 57% 0.4% blunt · at its level
source results share of the score
LiveBench model judgments 1 28% ▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards 1 19% ▇▇
Hallucinations Leaderboard results 4 14% ▇▇
LMArena leaderboard dataset 1 14% ▇▇
Polyrating: A Cost-Effective and Bias-A… 1 12% ▇
LMSYS Chatbot Arena leaderboard Space: … 1 10% ▇
Open Portuguese LLM Leaderboard 1 3% ▇
prior: pull toward the middle 1% ▇
weighted average = 35.0
Licences: LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Hallucinations Leaderboard results ([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 7B v1.5 (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/vicuna-7B-v1.5-GGUF/tree/8b4a138d6ba32660c42b5df6dad7ad5c23b80c8c))
```sh
llama cli -hf TheBloke/vicuna-7B-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-7B-v1.5-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.2 GB) `llama cli -hf TheBloke/vicuna-7B-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 7B v1.5 (Q4_K_M, needs ~4.1 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/vicuna-7B-v1.5-GGUF/tree/8b4a138d6ba32660c42b5df6dad7ad5c23b80c8c))
```sh
lms get https://huggingface.co/TheBloke/vicuna-7B-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 (~7.2 GB) `lms get https://huggingface.co/TheBloke/vicuna-7B-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 7B v1.5 (Q4_K_M, needs ~4.7 GB, from [ollama.com](https://ollama.com/library/vicuna/tags))
```sh
ollama run vicuna:7b-v1.5-q4_K_M
```
Other versions: Q8_0 (~8.9 GB) `ollama run vicuna:7b-v1.5-q8_0`
Model repository and documentation: [huggingface.co/lmsys](https://huggingface.co/lmsys/vicuna-7b-v1.5)
[interactive version](https://whatmodel.sh/model/vicuna-7b-v1-5) · [JSON](https://whatmodel.sh/api?format=json&all=1)