whatmodel.sh / StableLM-Base-Alpha 7B v2
StableLM-Base-Alpha 7B v2
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StableLM-Base-Alpha 7B v2 is an open-weights language model from Stability AI, released August 2023. Its published size is 6.89B parameters and a 4k-token context window. Licence: cc-by-sa-4.0.
Catalogue identity notes
Maker repo stabilityai/stablelm-base-alpha-7b-v2 @6d17650: licence tag cc-by-sa-4.0. 6,889,414,656 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2023-08: month the maker's Hugging Face repo was created (createdAt 2023-08-04). Pretrained base checkpoint (not an instruct/chat version).
$ how it compares
The LLMs ranked either side of StableLM-Base-Alpha 7B v2, out of 828 with a score, then more from Stability AI.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 748 |  OpenLLaMA 7B v2 | 29±1 | local | ||
| 749 |  Gemma 1.1 2B IT | 29±1 | local | ||
| 750 | StableLM-Base-Alpha 7B v2 | 29±1 | local | ||
| 751 |  Granite 3.1 1B-A400M Instruct | 29±2 | local | ||
| 752 |  OPT-30B | 29±1 | local | ||
| more from Stability AI | |||||
| 718 | Stable Code 3B | 33±1 | local | ||
| 746 | StableLM-3B-4E1T | 29±1 | local | ||
| 785 | StableLM-Base-Alpha 3B v2 | 24±1 | local | ||
$ what it's good at
other tests12 not in a task · 100% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| ARC Challenge lab | 40.5% ✎ | ▁·▁··▁▁▇▂█▆▃ beats 14/26 | 45% |
| WinoGrande lab | 68.8% ✎ | ▁····▁··▁▂█▄ beats 21/26 | 12% |
| BoolQ lab | 70.3% ✎ | ▂▃▂▅▆▂▅▆█▂▂▂ beats 13/26 | 10% |
| ARC Easy lab | 69.1% ✎ | ▁▁·▁▁▄▅▄█▅▁▃ beats 16/26 | 7.0% |
| HellaSwag lab | 74.3% ✎ | ▃···▁▇▅█▇▃▁▁ beats 17/26 | 5.7% |
| SciQ lab | 93.9% ✎ | ▁·▁·▁·▃▅▃█▄▁ beats 20/26 | 5.4% |
| PIQA lab | 78.5% ✎ | ▁·▁··▁▁▃█▅█▂ beats 18/26 | 4.4% |
| OpenBookQA lab | 30.4% ✎ | █·▁▁▃·▁▃▅▇▃▄ beats 15/26 | 3.5% |
4 more tests
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LAMBADA (OpenAI version), last-word accuracy lab | 74.2% ✎ | ▁··▂▁▁▁▂▆█▅▃ beats 23/26 | 2.7% |
| Social IQA lab | 42.4% ✎ | ▁···▁▃█▁▁▁·▁ beats 15/26 | 2.5% |
| Open PT-LLM Leaderboard average (9 Portuguese tasks) | 44.6% ✎ | ▃▁▁▁▁▁▃▁▂█▅▁ beats 58/276 | 1.1% |
| TruthfulQA MC2 lab | 36.5% ✎ | █·█·█▄···▄·█ beats 6/9 | 0.6% |
$ where to run it
Other ways to run it
- Run it as published · needs ~27.56 GB · from huggingface.co/stabilityai (official)
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='stabilityai/stablelm-base-alpha-7b-v2', torch_dtype='auto', device_map='auto', trust_remote_code=True)('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
Official weights and documentation: huggingface.co/stabilityai ↗
$ how the score was worked out · Stability AI — original Stabl… 98%, Open Portuguese LLM Leaderboa… 1%
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
| source | results | share of the score |
|---|---|---|
 Stability AI — original StableLM zero-shot evaluation outputs · lab-reported | 11 | 98% |
 Open Portuguese LLM Leaderboard (archived) | 1 | 1% |
| prior · pull toward the middle | 0% | |
| weighted average | = 28.8 |
$ improve this page
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