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Llama-3-Instruct-8B-SimPO-v0.2

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Llama-3-Instruct-8B-SimPO-v0.2 is an open-weights language model from Princeton NLP, released July 2024. Its published size is 8.03B parameters.

Catalogue identity notes

Maker repo princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2 @2b18bdc: no licence tag on the card. 8,030,261,248 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-07: month the maker's Hugging Face repo was created (createdAt 2024-07-06).

38.5±1.1#555 of 719 LLMsfrom 4 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of Llama-3-Instruct-8B-SimPO-v0.2, out of 828 with a score, then more from Princeton NLP.

#modelscorewhereRAMreleased
645Code Llama 13B Instruct Meta39±1local~9.8GB2023-08
646MistralHermes-CodePro-7B-v1 beowolx39±1local~6.1GB2023-12
647Llama-3-Instruct-8B-SimPO-v0.2 Princeton NLP39±1local~6.6GB2024-07
648Llama 3.2 3B Instruct Meta38±1api $0.12/M +local~3.4GB2024-09
649Qwen-14B-Chat Alibaba38±1local~11GB2023-09
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
400Llama-3-Instruct-8B-SimPO Princeton NLP51±1local~6.6GB2024-05

see all LLMs · find the ones that fit your machine

$ what it's good at

math▁▃▃▅▆▅▄█▄▁▁▁ #379 of 398 * · 29% of its score · 1 test

Beats Command R (03-2024), Molmo 7B-D (0924), Mistral 7B Instruct v0.3 here, though they rank higher overall. Trails Gemma 2B (base), Llama 3.2 1B Instruct, SmolLM2 1.7B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
GSM8K (ZeroEval, zero-shot)57.5% ✎▁▄·▁▄█▅▅▅███ beats 3/43very easy · avg 89%▇▇▇▇▇▇▇▇▇▇▇▇ 29%sharp · at its level
other tests3 not in a task · 70% of its score
testresultvs the others in its rundifficultycountswhy
MMLU-Redux (ZeroEval, 2,778 questions)55.2% ✎▁·▁▃▄▄▄▅█▄▂▁ beats 2/53easy · avg 77%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 56%at its level
WildBench v2 WB-Score (task macro)37.1554 WB-Score ✎▁·▁▁·▃█▄▇▆▅▆ beats 24/52avg 45.199 WB-Score▇▇▇▇▇ 12%sharp
Open PT-LLM Leaderboard average (9 Portuguese tasks)68.4% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 153/276medium · avg 73%▇ 2.3%at its level · noisy

$ where to run it

llama.cpp · recommended

  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-v0.2 · needs ~4.9 GB · from huggingface.co/matrixportalx
    chat in the terminal
    llama cli -hf matrixportalx/Llama-3-Instruct-8B-SimPO-v0.2-Q4_K_M-GGUF:q4_k_m
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf matrixportalx/Llama-3-Instruct-8B-SimPO-v0.2-Q4_K_M-GGUF:q4_k_m

LM Studio

  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-v0.2 · needs ~4.9 GB · from huggingface.co/matrixportalx

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/matrixportalx/Llama-3-Instruct-8B-SimPO-v0.2-Q4_K_M-GGUF@q4_k_m -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Llama-3-Instruct-8B-SimPO-v0.2 · needs ~4.9 GB · from huggingface.co/matrixportalx
    chat in the terminal
    ollama run hf.co/matrixportalx/Llama-3-Instruct-8B-SimPO-v0.2-Q4_K_M-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-v0.2', 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 · WildEval ZeroEval published r… 85%, WildBench v2 leaderboard data… 12%, Open Portuguese LLM Leaderboa… 2%

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
WildEval ZeroEval published results2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 85%Apache-2.0
WildBench v2 leaderboard data (Ai2)1▇▇▇▇ 12%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇ 2%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 38.5

$ 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