whatmodel.sh / StripedHyena-Nous-7B

StripedHyena-Nous-7B

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StripedHyena-Nous-7B is an open-weights language model from Together AI, released December 2023. Its published size is 7.65B parameters. Licence: apache-2.0.

Catalogue identity notes

Comparison-only. Arena entry 'stripedhyena-nous-7b' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'stripedhyena-nous-7b' as 'StripedHyena-Nous-7B' (Together AI, Apache 2.0), linking https://huggingface.co/togethercomputer/StripedHyena-Nous-7B. Maker repo togethercomputer/StripedHyena-Nous-7B @d8a5c9f: licence tag apache-2.0. 7,646,024,704 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2023-12: month the maker's Hugging Face repo was created (createdAt 2023-12-04).

33.2±2.3comparison only: no current run option verifiedfrom 2 benchmark results across 2 sources* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of StripedHyena-Nous-7B, out of 1132 with a score.

#modelscorewhereRAMreleased
992StarCoder2 3B BigCode33±1local~3.4GB2024-02
993LLaVA-v1.6-Vicuna-13B LLaVA33±6local~9.9GB2024-01
994StripedHyena-Nous-7B Together AI33±2comparison only2023-12
995Granite 3B Code Base (2K) IBM33±1local~3.7GB2024-04
996MiniChat-1.5-3B GeneZC33±2local~3.4GB2023-11

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▅▆█▆▆▆▂ #537 of 579 * · 98% of its score · 2 tests

Beats Code Llama 70B Instruct, Phi-2 (2.7B), Qwen2.5 1.5B Instruct 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
Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024)1028.0 ✎▂▁▂▁▃▇█▆▇▄▂▂ beats 18/95avg 1201▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 49%blunt
LMArena (text)1121.6 ✎▁▁▁▃▃▃▃▆▆▇█▃ beats 23/379avg 1268▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 48%blunt

$ where to run it

No verified API or download recorded yet · add one

Weights and documentation: huggingface.co/togethercomputer ↗

$ how the score was worked out · Polyrating: A Cost-Effective… 49%, LMArena leaderboard dataset 48%

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
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 49%CC-BY-4.0
LMArena leaderboard dataset1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 48%CC BY 4.0
prior · pull toward the middle▇ 2%
weighted average= 33.2

$ improve this page

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data updated · sources and licences · json