whatmodel.sh / ChatGLM3 6B 128K

ChatGLM3 6B 128K

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ChatGLM3 6B 128K is an open-weights language model from Zhipu AI (THUDM), released January 2024.

Catalogue identity notes

Maker repo zai-org/chatglm3-6b-128k @6381a64 (requested as THUDM/chatglm3-6b-128k, which the HF API redirects to zai-org/chatglm3-6b-128k): no licence tag on the card. Released 2024-01: month the maker's Hugging Face repo was created (createdAt 2024-01-30).

33.6±4.1#611 of 719 LLMsfrom 3 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of ChatGLM3 6B 128K, out of 828 with a score, then more from Zhipu AI (THUDM).

#modelscorewhereRAMreleased
704Code Llama 7B (base) Meta34±1local~5.5GB2023-08
705Nous-Hermes-13b Nous Research34±2local~9.8GB2023-06
706ChatGLM3 6B 128K Zhipu AI (THUDM)34±4local~5.8GB2024-01
707Zephyr 7B beta Hugging Face34±1local~6.1GB2023-10
708PaLM 2 Chat (chat-bison@001) Google34±2comparison only2023-05
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

other tests3 not in a task · 93% of its score
testresultvs the others in its rundifficultycountswhy
RULER 64k56.0% ✎▁▃·▁·▂▃▄█▂·▁ beats 1/22easy · avg 83%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 52%at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)49.0% ✎▃▁▁▁▁▁▁▃▂█▅▁ beats 66/276medium · avg 73%▇▇▇▇▇▇▇▇▇▇▇ 27%at its level · noisy
RULER 128k42.0% ✎▄··▃▄▂▆██▄▄▂ beats 2/21easy · avg 68%▇▇▇▇▇ 13%at its level · 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 128K · needs ~4.1 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/chatglm3-6b-128k-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-128k-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 128K · 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-128k-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 128K · needs ~4.1 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/chatglm3-6b-128k-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~12.49 GB · from huggingface.co/zai-org
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='zai-org/chatglm3-6b-128k', torch_dtype='auto', device_map='auto', trust_remote_code=True)('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

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

$ how the score was worked out · NVIDIA RULER repository READM… 65%, Open Portuguese LLM Leaderboa… 27%

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
NVIDIA RULER repository README results table2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 65%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇▇▇▇▇▇▇▇ 27%Apache-2.0
prior · pull toward the middle▇▇ 7%
weighted average= 33.6

$ 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