[whatmodel.sh](../../README.md) / ChatGLM2-6B # ChatGLM2-6B Zhipu AI (THUDM) · released 2023-06 · 6B params · chatglm2-6b-license **score 27.5** ±1.6 · #680 of 732 · from 6 benchmark results across 5 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks ChatGLM2-6B among every model on that task or test, worst to best.}

task            vs the others         rank  counts  tests
chat & writing  ▁●▁▃▆▄▆█▆▇▇▃   #396 of 402     95%      5

chat & writing · #396 of 402 · 95% of its score

Beats [Phi-2 (2.7B)](phi-2.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Falcon 7B Instruct](falcon-7b-instruct.md) here, though they rank lower overall.

test                                                      result  vs the others in its run  difficulty                counts  why
LMArena (text)                                            1024.8  ▁●▁▂▃▃▃▆▆▇█▃ beats 6/385  avg 1260                     27%  blunt
Polyrating rating (bias-aware re-…                         930.0  ▂▁●▁▃▇█▆▇▄▂▂ beats 7/95   avg 1187                     24%  typical
MT-Bench (GPT-4 single-answer gra…  4.9600 MT-Bench score (1-10)  ▆▂▂▆●▄██▂▄▄▆ beats 8/28   avg 8.539 MT-Bench scor…     19%  sharp · noisy
AlpacaEval 2 length-controlled                              4.4%  ▁●▂▃▆▆█▆▄▄▁▁ beats 5/122  medium · avg 35%             18%  sharp · too hard for it
AlpacaEval 1.0 win rate (vs text-…                         47.1%  ▂▂·●·▇▃▆█▆▄▂ beats 7/45   very easy · avg 94%         7.5%  sharp · at its level · noisy

other tests (1)

test                                result  vs the others in its run   difficulty        counts  why
Open PT-LLM Leaderboard average (…   47.2%  ▃▁▁▁▁▁▁●▂█▅▁ beats 62/276  medium · avg 72%    3.6%  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
LMArena leaderboard dataset                     1   27% ▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards             2   25% ▇▇▇
Polyrating: A Cost-Effective and Bias-A…        1   24% ▇▇▇
LMSYS Chatbot Arena leaderboard Space: …        1   19% ▇▇
Open Portuguese LLM Leaderboard                 1    4% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 27.5

_{Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · LMSYS Chatbot Arena leaderboard Space: MT-Bench column of the published leaderboard table ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · 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 (fp16, needs ~12.49 GB, NVIDIA only, from [github.com/vllm-project](https://github.com/vllm-project/vllm/blob/64cb68384241f046d07ba3797e1b14dbc82a0b2d/docs/models/supported_models.md)) ```sh vllm serve zai-org/chatglm2-6b --trust-remote-code ``` Model repository and documentation: [huggingface.co/zai-org](https://huggingface.co/zai-org/chatglm2-6b) [interactive version](https://whatmodel.sh/model/chatglm2-6b) · [JSON](https://whatmodel.sh/api?format=json&all=1)