[whatmodel.sh](../../README.md) / Baichuan2-7B-Base # Baichuan2-7B-Base Baichuan · released 2023-08 · other **score 30.4** ±1.0 · #659 of 732 · from 11 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 Baichuan2-7B-Base among every model on that task or test, worst to best.}

other tests (11)

test                                result  vs the others in its run   difficulty           counts  why
ARC Challenge                        43.2%  ▁·▁··▁▁▇▂●▆▃ beats 18/26   easy · avg 82%          49%  sharp
WinoGrande                           67.6%  ▁····▁··▁▂●▄ beats 18/26   easy · avg 86%          11%  at its level
BoolQ                                73.1%  ▂▃▂▅▆▂▅●█▂▂▂ beats 17/26   easy · avg 88%         8.3%  at its level
HellaSwag                            72.3%  ▃···▁▇▅●▇▃▁▁ beats 15/26   very easy · avg 91%    5.9%  at its level
ARC Easy                             72.8%  ▁▁·▁▁▄▅▄█●▁▃ beats 21/26   easy · avg 85%         5.6%  blunt · at its level
SciQ                                 94.6%  ▁·▁·▁·▃▅▃█●▁ beats 24/26   very easy · avg 97%    4.5%  blunt
PIQA                                 76.2%  ▁·▁··▁▁▃●▅█▂ beats 10/26   easy · avg 87%         4.1%  blunt · at its level
OpenBookQA                           30.4%  █·▁▁▃·▁▃●▇▃▄ beats 16/26   hard · avg 49%         4.0%  too hard for it
LAMBADA (OpenAI version), last-wo…   71.0%  ▁··▂▁▁▁▂▆●▅▃ beats 17/26   very easy · avg 86%    2.5%  blunt · at its level
Social IQA                           41.8%  ▁···▁▃●▁▁▁·▁ beats 8/26    hard · avg 56%         2.4%  blunt
Open PT-LLM Leaderboard average (…   46.8%  ▃▁▁▁▁▁●▁▂█▅▁ beats 61/276  medium · avg 72%       1.8%  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                          10   98% ▇▇▇▇▇▇▇▇▇▇▇▇
Open Portuguese LLM Leaderboard        1    2% ▇
prior: pull toward the middle               0% ▇
weighted average                          = 30.4

_{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 (BF16, needs ~15.01 GB, from [huggingface.co/baichuan-inc](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/tree/f9d4d8dd2f7a3dbede3bda3b0cf0224e9272bbe5)) ```sh uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='baichuan-inc/Baichuan2-7B-Base', torch_dtype='auto', device_map='auto', trust_remote_code=True)('Once upon a time', max_new_tokens=40)[0]['generated_text'])" ``` Model repository and documentation: [huggingface.co/baichuan-inc](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) [interactive version](https://whatmodel.sh/model/baichuan2-7b-base) · [JSON](https://whatmodel.sh/api?format=json&all=1)