whatmodel.sh / SPPO-Llama-3-8B-Instruct-GPM-2B

SPPO-Llama-3-8B-Instruct-GPM-2B

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

SPPO-Llama-3-8B-Instruct-GPM-2B is an open-weights language model from general-preference (GPM), released October 2024. Its published size is 8.03B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo general-preference/SPPO-Llama-3-8B-Instruct-GPM-2B @c004c9b: licence tag apache-2.0. 8,030,261,248 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-10: month the maker's Hugging Face repo was created (createdAt 2024-10-08).

48.8±1.6#478 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 SPPO-Llama-3-8B-Instruct-GPM-2B, out of 1132 with a score, then more from general-preference (GPM).

#modelscorewhereRAMreleased
609Nova Lite Amazon49±2api $0.10/M2024-12
610MegaBeam-Mistral-7B-512k AWS (aws-prototyping)49±4local~6.1GB2024-07
611SPPO-Llama-3-8B-Instruct-GPM-2B general-preference (GPM)49±2local~18GB2024-10
612GPT-4o Audio Preview (2024-12-17) OpenAI49±2comparison only2024-12
613Claude 3 Sonnet Anthropic49±1comparison only2024-02
more from general-preference (GPM)
572GPO-Llama-3-8B-Instruct-GPM-2B general-preference (GPM)50±2local~18GB2024-10

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #291 of 466 * · 99% of its score · 1 test

Beats Ministral 3 14B Reasoning 2512, Ministral 3 14B, Phi-4 14B here, though they rank higher overall. Trails Flan-T5 XXL, LFM2 1.2B, Granite 4.0 H 1B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled35.3% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 90/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%at its level

$ where to run it

Other ways to run it

  1. Run it as published · needs ~16.06 GB · from huggingface.co/general-preference (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='general-preference/SPPO-Llama-3-8B-Instruct-GPM-2B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/general-preference ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 99%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 48.8

$ 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