whatmodel.sh / PlatoLM-7B

PlatoLM-7B

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

PlatoLM-7B is an open-weights language model from FreedomIntelligence (CUHK-SZ), released August 2023. Licence: apache-2.0.

Catalogue identity notes

Maker repo FreedomIntelligence/PlatoLM-7B @e0fdb73 (requested as FreedomIntelligence/PlatoLM-7b, which the HF API redirects to FreedomIntelligence/PlatoLM-7B): licence tag apache-2.0. Released 2023-08: month the maker's Hugging Face repo was created (createdAt 2023-08-21).

35.6±2.2#798 of 949 LLMsfrom 2 benchmark results across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of PlatoLM-7B, out of 1132 with a score, then more from FreedomIntelligence (CUHK-SZ).

#modelscorewhereRAMreleased
962Llama 3 8B (base) Meta36±1local~6.6GB2024-04
963Trurl 2 13B Academic Voicelab36±3local~29GB2023-09
964PlatoLM-7B FreedomIntelligence (CUHK-SZ)36±2local~30GB2023-08
965Mistral 7B v0.1 (base) Mistral AI36±0local~5.8GB2023-09
966Qwen2.5 1.5B Instruct Alibaba36±1local~2.4GB2024-09
more from FreedomIntelligence (CUHK-SZ)
318HuatuoGPT-o1-72B FreedomIntelligence (CUHK-SZ)59±4local~28GB2024-12
432HuatuoGPT-o1-70B FreedomIntelligence (CUHK-SZ)55±4local~22GB2024-12
522AceGPT-v2-8B-Chat FreedomIntelligence (CUHK-SZ)52±5local~6.6GB2024-06

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #427 of 466 * · 98% of its score · 2 tests

Beats Gemma 4 E2B, Code Llama 70B Instruct, Phi-2 (2.7B) here, though they rank higher overall. Trails Gemma 3 270M IT, SmolLM2 360M Instruct, Granite 4.0 350M here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled10.5% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 25/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 79%typical
AlpacaEval 1.0 win rate (vs text-davinci-003, GPT-4 judge)81.9% ✎▂▂·▆·▇▃▆█▆▄▂ beats 20/45very easy · avg 96%▇▇▇▇▇▇▇▇ 19%sharp · noisy

$ where to run it

Other ways to run it

  1. Run it as published · needs ~26.95 GB · from huggingface.co/FreedomIntelligence (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='FreedomIntelligence/PlatoLM-7B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/FreedomIntelligence ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 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
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 98%Apache-2.0
prior · pull toward the middle▇ 2%
weighted average= 35.6

$ 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