whatmodel.sh / Vicuna 7B v1.5 16K

Vicuna 7B v1.5 16K

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Vicuna 7B v1.5 16K is an open-weights language model from LMSYS, released July 2023. Licence: llama2.

Catalogue identity notes

Maker repo lmsys/vicuna-7b-v1.5-16k @c8df3ca: licence tag llama2. Released 2023-07: month the maker's Hugging Face repo was created (createdAt 2023-07-31).

34.2±2.2#812 of 949 LLMsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Vicuna 7B v1.5 16K, out of 1132 with a score, then more from LMSYS.

#modelscorewhereRAMreleased
977StarCoder 15.5B BigCode35±1local~11GB2023-04
978Granite 3.1 3B-A800M Instruct IBM35±2local~3.6GB2024-12
979Vicuna 7B v1.5 16K LMSYS34±2local~5.8GB2023-07
980MedReason-8B UCSC VLAA34±5local~18GB2025-04
981Llama 2 13B (base) Meta34±1local~9.3GB2023-07
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

other tests1 not in a task · 98% of its score
testresultvs the others in its rundifficultycountswhy
LiveBench 2024-07-2614.5% ✎▁▁▁▂▃▃▂█▆▃▅▃ beats 8/61medium · avg 41%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 98%typical

$ 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 v1.5 16K · needs ~4.1 GB · from huggingface.co/TheBloke
    chat in the terminal
    llama cli -hf TheBloke/vicuna-7B-v1.5-16K-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf TheBloke/vicuna-7B-v1.5-16K-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 v1.5 16K · needs ~4.1 GB · from huggingface.co/TheBloke

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/TheBloke/vicuna-7B-v1.5-16K-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 v1.5 16K · needs ~4.1 GB · from huggingface.co/TheBloke
    chat in the terminal
    ollama run hf.co/TheBloke/vicuna-7B-v1.5-16K-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-v1.5-16k', 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 · LiveBench model judgments (HF… 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
LiveBench model judgments (HF livebench/model_judgment, 2024-10-22 revision) with the LiveBench question datasets1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 98%Apache-2.0 (stated in the LiveBench datasheet)
prior · pull toward the middle▇ 2%
weighted average= 34.2

$ 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