whatmodel.sh / Llama-3-Instruct-8B-SPPO-Iter3

Llama-3-Instruct-8B-SPPO-Iter3

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Llama-3-Instruct-8B-SPPO-Iter3 is an open-weights language model from UCLA AGI (SPPO), released June 2024. Its published size is 8.03B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3 @48c29bf: licence tag apache-2.0. 8,030,261,248 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-06: month the maker's Hugging Face repo was created (createdAt 2024-06-25).

49.9±1.6#446 of 949 LLMsfrom 2 benchmark results across 2 sources* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Llama-3-Instruct-8B-SPPO-Iter3, out of 1132 with a score, then more from UCLA AGI (SPPO).

#modelscorewhereRAMreleased
575Yi 34B 200K (base) 01.AI50±5local~23GB2023-11
576Yi-1.5 34B Chat 16K 01.AI50±4local~22GB2024-05
577Llama-3-Instruct-8B-SPPO-Iter3 UCLA AGI (SPPO)50±2local~6.6GB2024-06
578Gemma 2 27B IT Google50±1local~18GB2024-06
579Bielik 11B v2.3 Instruct SpeakLeash50±2local~8.6GB2024-08
more from UCLA AGI (SPPO)
402Gemma-2-9B-It-SPPO-Iter3 UCLA AGI (SPPO)56±2local~7GB2024-06
681Mistral7B-PairRM-SPPO UCLA AGI (SPPO)47±2local~6.1GB2024-05

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #277 of 466 * · 94% of its score · 1 test

Beats Ministral 3 14B Reasoning 2512, Ministral 3 14B, OpenThinker2 32B here, though they rank higher overall. Trails Flan-T5 XXL, Granite 4.0 H 1B, Granite 4.0 1B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled38.6% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 96/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 94%at its level
other tests1 not in a task · 4.8% of its score
testresultvs the others in its rundifficultycountswhy
Open PT-LLM Leaderboard average (9 Portuguese tasks)69.1% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 158/276medium · avg 73%▇▇ 4.8%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-SPPO-Iter3 · needs ~4.9 GB · from huggingface.co/bartowski
    chat in the terminal
    llama cli -hf bartowski/Llama-3-Instruct-8B-SPPO-Iter3-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-SPPO-Iter3-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-SPPO-Iter3 · 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-SPPO-Iter3-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-SPPO-Iter3 · needs ~4.9 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/Llama-3-Instruct-8B-SPPO-Iter3-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~16.06 GB · from huggingface.co/UCLA-AGI (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/UCLA-AGI ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 94%, Open Portuguese LLM Leaderboa… 5%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 94%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇ 5%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 49.9

$ 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