[whatmodel.sh](../../README.md) / ChatGLM3-6B # ChatGLM3-6B Zhipu AI (THUDM) · released 2023-10 · 6.24B params · chatglm3-6b-license **score 29.9** ±2.0 · #661 of 732 · from 4 benchmark results across 4 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks ChatGLM3-6B among every model on that task or test, worst to best.}

task            vs the others          rank  counts  tests
agent tasks     ▁▁●▆█▄▅▅▄▄▂▁   #295 of 313*     10%      1
chat & writing  ▁▁●▃▆▄▆█▆▇▇▃   #394 of 402*     81%      2

agent tasks · #295 of 313 · 10% of its score

Beats [GLM-4 9B Chat](glm-4-9b-chat.md), [DBRX Instruct](dbrx-instruct.md), [QwQ-32B-Preview](qwq-32b-preview.md) here, though they rank higher overall. Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md) here, though they rank lower overall.

test                               result  vs the others in its run  difficulty      counts  why
BFCL (version not stated) (maker)   57.9%  ●·········██ beats 0/5    easy · avg 77%     10%  blunt · at its level

chat & writing · #394 of 402 · 81% 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), [Granite 4.0 350M](granite-4-0-350m.md) here, though they rank lower overall.

test                                result  vs the others in its run  difficulty  counts  why
LMArena (text)                      1056.6  ▁▁●▂▃▃▃▆▆▇█▃ beats 8/385  avg 1260       42%  blunt
Polyrating rating (bias-aware re-…   946.0  ▂▁●▁▃▇█▆▇▄▂▂ beats 9/95   avg 1187       39%  typical

other tests (1)

test                                result  vs the others in its run   difficulty        counts  why
Open PT-LLM Leaderboard average (…   56.0%  ▃▁▁▁▁▁▁▁●█▅▁ beats 79/276  medium · avg 72%    6.6%  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   42% ▇▇▇▇▇
Polyrating: A Cost-Effective and Bias-A…        1   39% ▇▇▇▇▇
ChatGLM: A Family of Large Language Mod…        1   10% ▇
Open Portuguese LLM Leaderboard                 1    7% ▇
prior: pull toward the middle                        2% ▇
weighted average                                   = 29.9

_{Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))} ## How to run it ### llama.cpp (recommended) 1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) ```sh # macOS / Linux curl -LsSf https://llama.app/install.sh | sh # Windows irm https://llama.app/install.ps1 | iex ``` 2. Run ChatGLM3-6B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/chatglm3-6b-GGUF/tree/b79a62fe07eb5135732d341cd8cce1777e4a7788)) ```sh llama cli -hf mradermacher/chatglm3-6b-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf mradermacher/chatglm3-6b-GGUF:Q4_K_M ``` Other versions: Q8_0 (~6.6 GB) `llama cli -hf mradermacher/chatglm3-6b-GGUF:Q8_0` ### LM Studio 1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download) 2. Run ChatGLM3-6B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/chatglm3-6b-GGUF/tree/b79a62fe07eb5135732d341cd8cce1777e4a7788)) ```sh lms get https://huggingface.co/mradermacher/chatglm3-6b-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: Q8_0 (~6.6 GB) `lms get https://huggingface.co/mradermacher/chatglm3-6b-GGUF@Q8_0 -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run ChatGLM3-6B (Q4_K_M, needs ~4.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/chatglm3-6b-GGUF/tree/b79a62fe07eb5135732d341cd8cce1777e4a7788)) ```sh ollama run hf.co/mradermacher/chatglm3-6b-GGUF:Q4_K_M ``` Other versions: Q8_0 (~6.6 GB) `ollama run hf.co/mradermacher/chatglm3-6b-GGUF:Q8_0` ### 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/chatglm3-6b --trust-remote-code ``` Model repository and documentation: [huggingface.co/zai-org](https://huggingface.co/zai-org/chatglm3-6b) [interactive version](https://whatmodel.sh/model/chatglm3-6b) · [JSON](https://whatmodel.sh/api?format=json&all=1)