[whatmodel.sh](../../README.md) / Vicuna 7B v1.3
# Vicuna 7B v1.3
LMSYS · released 2023-06
**score 34.8** ±2.1 · #631 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 7B v1.3 among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁●▆▄▆█▆▇▇▃ #382 of 402* 78% 2
chat & writing · #382 of 402 · 78% 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 7.2% ▁▂▂●▆▆█▆▄▄▁▁ beats 15/122 medium · avg 35% 66% sharp
AlpacaEval 1.0 win rate (vs text-… 76.8% ▂▂·▆·▇●▆█▆▄▂ beats 17/45 very easy · avg 94% 12% sharp · at its level · noisy
other tests (2)
test result vs the others in its run difficulty counts why
VEC embodied concepts, zero-shot … 55.8% ▁····▁▁·▁·▃● beats 9/11 hard · avg 63% 20% typical
VEC visual concepts, zero-shot pe… 63.7% ▅▅▃●·▃··▃·▃▃ beats 6/11 hard · avg 66% 0.3% blunt · at its level
source results share of the score
AlpacaEval 1.0 and 2.0 leaderboards 2 78% ▇▇▇▇▇▇▇▇▇
Can Language Models Understand Physical… 2 20% ▇▇
prior: pull toward the middle 2% ▇
weighted average = 34.8
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
### 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 v1.3 (Q4_K_M, needs ~4.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/vicuna-7b-v1.3-GGUF/tree/2dcb0b3fc63f4c3a737e34188bbbe203177b17ff))
```sh
llama cli -hf mradermacher/vicuna-7b-v1.3-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/vicuna-7b-v1.3-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.2 GB) `llama cli -hf mradermacher/vicuna-7b-v1.3-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 v1.3 (Q4_K_M, needs ~4.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/vicuna-7b-v1.3-GGUF/tree/2dcb0b3fc63f4c3a737e34188bbbe203177b17ff))
```sh
lms get https://huggingface.co/mradermacher/vicuna-7b-v1.3-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/mradermacher/vicuna-7b-v1.3-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 v1.3 (Q4_K_M, needs ~4.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/vicuna-7b-v1.3-GGUF/tree/2dcb0b3fc63f4c3a737e34188bbbe203177b17ff))
```sh
ollama run hf.co/mradermacher/vicuna-7b-v1.3-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.2 GB) `ollama run hf.co/mradermacher/vicuna-7b-v1.3-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (fp16, needs ~13.48 GB, from [huggingface.co/lmsys](https://huggingface.co/lmsys/vicuna-7b-v1.3/tree/236eeeab96f0dc2e463f2bebb7bb49809279c6d6))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='lmsys/vicuna-7b-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-7b-v1.3)
[interactive version](https://whatmodel.sh/model/vicuna-7b-v1-3) · [JSON](https://whatmodel.sh/api?format=json&all=1)