whatmodel.sh / ChatGLM3-6B

ChatGLM3-6B

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ChatGLM3-6B is an open-weights language model from Zhipu AI (THUDM), released October 2023. Its published size is 6.24B parameters. Licence: chatglm3-6b-license.

Catalogue identity notes

Comparison-only. Arena entry 'chatglm3-6b' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'chatglm3-6b' as 'ChatGLM3-6B' (Tsinghua, Apache-2.0), linking https://huggingface.co/THUDM/chatglm3-6b. Maker repo zai-org/chatglm3-6b @e9e0406: licence tag none tagged. 6,243,584,032 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2023-10: month the maker's Hugging Face repo was created (createdAt 2023-10-25).

Model information sources

· Weights licence ↗

27.0±2.1#661 of 719 LLMsfrom 4 benchmark results across 4 sources

$ how it compares

The LLMs ranked either side of ChatGLM3-6B, out of 828 with a score, then more from Zhipu AI (THUDM).

#modelscorewhereRAMreleased
760Granite 3.0 1B-A400M Instruct IBM27±1local~2.3GB2024-10
761RedPajama-INCITE 7B Base Together27±1local~16GB2023-06
762ChatGLM3-6B Zhipu AI (THUDM)27±2local~5.8GB2023-10
763Granite 4.0 H 350M IBM27±1local~1.7GB2025-10
764Flan-T5 Large Google27±2local~4.8GB2022-10
more from Zhipu AI (THUDM)
349GLM-4 9B Chat 1M Zhipu AI (THUDM)53±5local~8.1GB2024-06
485CogVLM2 Llama3 Chinese Chat 19B Zhipu AI (THUDM)47±7comparison only2024-05
569CogVLM2 Llama3 Chat 19B Zhipu AI (THUDM)43±3local~43GB2024-05

see all LLMs · find the ones that fit your machine

$ what it's good at

agent tasks▁▁▃▆█▆▅▅▅▄▃▁ #283 of 300 * · 10% of its score · 1 test

Beats GLM-4 9B Chat, DBRX Instruct, Olmo 3 7B Instruct here, though they rank higher overall. Trails Granite 4.0 H 350M here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
BFCL (version not stated) lab57.9% ✎█·········██ beats 0/5easy · avg 78%▇▇▇▇ 10%blunt · at its level
chat & writing▁▁▃▃▅▄▅█▆▆▇▃ #385 of 393 * · 82% of its score · 2 tests

Beats Phi-2 (2.7B) here, though they rank higher overall. Trails Gemma 3 270M IT, SmolLM2 360M Instruct, Granite 4.0 350M here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024)946.0 ✎▂▁▃▁▃▇█▆▇▄▂▂ beats 9/95avg 1201▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 41%blunt
LMArena (text)1056.5 ✎▁▁▃▂▃▃▃▆▆▇█▃ beats 8/379avg 1268▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 41%blunt
other tests1 not in a task · 5.6% of its score
testresultvs the others in its rundifficultycountswhy
Open PT-LLM Leaderboard average (9 Portuguese tasks)56.0% ✎▃▁▁▁▁▁▁▁▃█▅▁ beats 79/276medium · avg 73%▇▇ 5.6%noisy

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run ChatGLM3-6B · needs ~4.1 GB · from huggingface.co/mradermacher
    chat in the terminal
    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)
    llama serve -hf mradermacher/chatglm3-6b-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run ChatGLM3-6B · needs ~4.1 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/chatglm3-6b-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run ChatGLM3-6B · needs ~4.1 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/chatglm3-6b-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~12.49 GB, NVIDIA only · from github.com/vllm-project
    vllm serve zai-org/chatglm3-6b --trust-remote-code

Weights and documentation: huggingface.co/zai-org ↗

$ how the score was worked out · Polyrating: A Cost-Effective… 41%, LMArena leaderboard dataset 41%, ChatGLM: A Family of Large La… 10% and 1 more

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
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3)1▇▇▇▇▇▇▇▇▇▇▇▇ 41%CC-BY-4.0
LMArena leaderboard dataset1▇▇▇▇▇▇▇▇▇▇▇▇ 41%CC BY 4.0
ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools (Team GLM, arXiv 2406.12793v2) · lab-reported1▇▇▇ 10%CC BY 4.0
Open Portuguese LLM Leaderboard (archived)1▇▇ 6%Apache-2.0
prior · pull toward the middle▇ 2%
weighted average= 27.0

$ 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