whatmodel.sh / Llama-3-Instruct-8B-SimPO

Llama-3-Instruct-8B-SimPO

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Llama-3-Instruct-8B-SimPO is an open-weights language model from Princeton NLP, released May 2024. Its published size is 8.03B parameters.

Catalogue identity notes

Maker repo princeton-nlp/Llama-3-Instruct-8B-SimPO @b3d3584: no licence tag on the card. 8,030,261,248 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-05: month the maker's Hugging Face repo was created (createdAt 2024-05-17).

50.7±1.4#331 of 719 LLMsfrom 3 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of Llama-3-Instruct-8B-SimPO, out of 828 with a score, then more from Princeton NLP.

#modelscorewhereRAMreleased
398LFM2 24B-A2B Liquid AI51±1local~16GB2026-02
399AceReason-Nemotron-1.0-14B NVIDIA51±2local~10GB2025-05
400Llama-3-Instruct-8B-SimPO Princeton NLP51±1local~6.6GB2024-05
401Athene-70B Nexusflow51±1local~29GB2024-07
402Qwen2 72B Instruct Alibaba51±1local~33GB2024-06
more from Princeton NLP
247Gemma 2 9B IT SimPO Princeton NLP59±1local~7.6GB2024-07
248Gemma 2 9B IT DPO (Princeton) Princeton NLP59±1local~7.6GB2024-07
408Llama-3-8B-ProLong-512k-Instruct Princeton NLP50±5local~35GB2024-08

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #234 of 393 * · 75% of its score · 1 test

Beats Mistral Small 4 (119B-A6.5B), Ministral 3 14B Reasoning 2512, Qwen2.5 Coder 32B Instruct here, though they rank higher overall. Trails Flan-T5 XXL, LFM2.5 1.2B Instruct, Gemma 3 4B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled44.6% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 101/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 75%at its level
other tests2 not in a task · 24% of its score
testresultvs the others in its rundifficultycountswhy
WildBench v2 WB-Score (task macro)37.0497 WB-Score ✎▁·▁▁·▃█▄▇▆▅▆ beats 23/52avg 45.199 WB-Score▇▇▇▇▇▇▇▇ 20%sharp
Open PT-LLM Leaderboard average (9 Portuguese tasks)68.3% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 152/276medium · avg 73%▇ 3.7%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 Llama-3-Instruct-8B-SimPO · needs ~4.9 GB · from huggingface.co/bartowski
    chat in the terminal
    llama cli -hf bartowski/Llama-3-Instruct-8B-SimPO-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf bartowski/Llama-3-Instruct-8B-SimPO-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 Llama-3-Instruct-8B-SimPO · needs ~4.9 GB · from huggingface.co/bartowski

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/bartowski/Llama-3-Instruct-8B-SimPO-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 Llama-3-Instruct-8B-SimPO · needs ~4.9 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/Llama-3-Instruct-8B-SimPO-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~16.06 GB · from huggingface.co/princeton-nlp (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='princeton-nlp/Llama-3-Instruct-8B-SimPO', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/princeton-nlp ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 75%, WildBench v2 leaderboard data… 20%, Open Portuguese LLM Leaderboa… 4%

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)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 75%Apache-2.0
WildBench v2 leaderboard data (Ai2)1▇▇▇▇▇▇ 20%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇ 4%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 50.7

$ 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