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Vicuna 7B v1.3

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Vicuna 7B v1.3 is an open-weights language model from LMSYS, released June 2023.

Catalogue identity notes

Maker repo lmsys/vicuna-7b-v1.3 @236eeea: no licence tag on the card. Released 2023-06: month the maker's Hugging Face repo was created (createdAt 2023-06-18).

32.6±2.4#832 of 949 LLMsfrom 2 benchmark results across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Vicuna 7B v1.3, out of 1132 with a score, then more from LMSYS.

#modelscorewhereRAMreleased
1001Stable Code 3B Stability AI33±1local~3.3GB2024-01
1002OLMo 2 1B Instruct (0425) Allen Institute for AI33±2local~2.4GB2025-04
1003Vicuna 7B v1.3 LMSYS33±2local~5.8GB2023-06
1004OLMo 7B Twin 2T Allen Institute for AI32±7local~30GB2024-02
1005DeepSeek-Coder 1.3B Base DeepSeek32±1local~2.4GB2023-10
more from LMSYS
888Vicuna 33B v1.3 LMSYS39±1local~23GB2023-06
949Vicuna 13B v1.5 LMSYS36±2local~9.8GB2023-08
960Vicuna 13B v1.3 LMSYS36±2local~29GB2023-06

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #440 of 466 * · 98% of its score · 2 tests

Beats Phi-2 (2.7B), Qwen2.5 1.5B Instruct, Minotaur 13B (fixed) here, though they rank higher overall. Trails Gemma 3 270M IT, SmolLM2 360M Instruct, Granite 4.0 350M here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled7.2% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 15/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 71%typical
AlpacaEval 1.0 win rate (vs text-davinci-003, GPT-4 judge)76.8% ✎▂▂·▆·▇▃▆█▆▄▂ beats 17/45very easy · avg 96%▇▇▇▇▇▇▇▇▇▇▇ 27%sharp · at its level · noisy

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Vicuna 7B v1.3 · needs ~4.1 GB · from huggingface.co/mradermacher
    chat in the terminal
    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)
    llama serve -hf mradermacher/vicuna-7b-v1.3-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Vicuna 7B v1.3 · needs ~4.1 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/vicuna-7b-v1.3-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Vicuna 7B v1.3 · needs ~4.1 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/vicuna-7b-v1.3-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~13.48 GB · from huggingface.co/lmsys (official)
    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'])"

Official weights and documentation: huggingface.co/lmsys ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 98%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The 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's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 98%Apache-2.0
prior · pull toward the middle▇ 2%
weighted average= 32.6

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json