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Baichuan-13B-Chat

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Baichuan-13B-Chat is an open-weights language model from Baichuan, released July 2023.

Catalogue identity notes

Maker repo baichuan-inc/Baichuan-13B-Chat @e3e1498: no licence tag on the card. Released 2023-07: month the maker's Hugging Face repo was created (createdAt 2023-07-08).

20.1±3.4#919 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 Baichuan-13B-Chat, out of 1132 with a score, then more from Baichuan.

#modelscorewhereRAMreleased
1097Flan-T5 Base Google20±3local~2.5GB2022-10
1098Falcon-RW 1B TII20±2local~4.3GB2023-04
1099Baichuan-13B-Chat Baichuan20±3local~29GB2023-07
1100OPT-2.7B Meta19±1local~7.1GB2022-05
1101SmolLM2 360M Instruct Hugging Face19±3local~1.7GB2024-10
more from Baichuan
387Baichuan-M2-32B Baichuan56±4local~22GB2025-08
454Baichuan-M1-14B-Instruct Baichuan54±4local~10GB2025-01
1033Baichuan2-7B-Base Baichuan30±1local~17GB2023-08

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▃▁▁▄▆▅▆█▆▆▆▂ #464 of 466 * · 95% of its score · 2 tests

Trails Gemma 3 270M IT, SmolLM2 360M Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 1.0 win rate (vs text-davinci-003, GPT-4 judge)21.8% ✎▃▂·▆·▇▃▆█▆▄▂ beats 1/45very easy · avg 96%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 52%sharp · at its level · noisy
AlpacaEval 2 length-controlled2.1% ✎▃▂▂▃▆▆█▆▄▄▁▁ beats 0/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 43%too hard for it

$ where to run it

Other ways to run it

  1. Run it as published · needs ~26.53 GB · from huggingface.co/baichuan-inc (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='baichuan-inc/Baichuan-13B-Chat', 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/baichuan-inc ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 95%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 95%Apache-2.0
prior · pull toward the middle▇▇ 5%
weighted average= 20.1

$ 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