[whatmodel.sh](../../README.md) / StableLM-Base-Alpha 3B # StableLM-Base-Alpha 3B Stability AI · released 2023-04 · 3.6B params · cc-by-sa-4.0 **score 0.5** ±1.7 · #724 of 732 · from 12 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks StableLM-Base-Alpha 3B among every model on that task or test, worst to best.
other tests (12) 
test                                result  vs the others in its run  difficulty           counts  why
SciQ (maker)                         71.7%  ●·▁·▁·▃▅▃█▄▁ beats 0/26   very easy · avg 97%     31%  blunt · at its level
ARC Easy (maker)                     42.0%  ●▁·▁▁▄▅▄█▅▁▃ beats 0/26   easy · avg 85%          12%  blunt
HellaSwag (maker)                    38.3%  ●···▁▇▅█▇▃▁▁ beats 0/26   very easy · avg 91%     11%  typical
PIQA (maker)                         63.8%  ●·▁··▁▁▃█▅█▂ beats 0/26   easy · avg 87%         9.4%  blunt · at its level
BoolQ (maker)                        57.6%  ●▃▂▅▆▂▅▆█▂▂▂ beats 0/26   easy · avg 88%         8.3%  typical
LAMBADA (OpenAI version), last-wo…   41.7%  ●··▂▁▁▁▂▆█▅▃ beats 0/26   very easy · avg 86%    8.0%  blunt · at its level
ARC Challenge (maker)                25.8%  ●·▁··▁▁▇▂█▆▃ beats 0/26   easy · avg 82%         7.7%  sharp · too hard for it
WinoGrande (maker)                   52.6%  ▁····●··▁▂█▄ beats 1/26   easy · avg 86%         6.5%  too hard for it
Social IQA (maker)                   35.6%  ●···▁▃█▁▁▁·▁ beats 0/26   hard · avg 56%         2.2%  blunt · too hard for it
OpenBookQA (maker)                   17.0%  ●·▁▁▃·▁▃▅▇▃▄ beats 5/26   hard · avg 49%         0.9%  too hard for it
TruthfulQA MC2 (maker)               40.5%  █·█·█▄···▄·● beats 9/9    medium · avg 55%       0.8%  blunt · at its level
Open PT-LLM Leaderboard average (…   27.6%  ●▁▁▁▁▁▁▁▂█▅▁ beats 2/276  medium · avg 72%       0.6%  too hard for it · noisy
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
Stability AI (submitted / maker-reporte…       11   98% ▇▇▇▇▇▇▇▇▇▇▇▇
Open Portuguese LLM Leaderboard                 1    1% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 0.5
Licences: Stability AI ([Apache-2.0](../../licenses/stablelm-original-eval-json-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) ## How to run it ### Other ways to run it 1. Run it as published (fp32, needs ~14.82 GB, from [huggingface.co/stabilityai](https://huggingface.co/stabilityai/stablelm-base-alpha-3b/tree/819657db42861818efac86e8ca60f673fa912ef8)) ```sh uv run --with torch --with 'transformers<5' python -c "from transformers import pipeline; print(pipeline('text-generation', model='stabilityai/stablelm-base-alpha-3b')('Once upon a time', max_new_tokens=40)[0]['generated_text'])" ``` Model repository and documentation: [huggingface.co/stabilityai](https://huggingface.co/stabilityai/stablelm-base-alpha-3b) [interactive version](https://whatmodel.sh/model/stablelm-base-alpha-3b) · [JSON](https://whatmodel.sh/api?format=json&all=1)