[whatmodel.sh](../../README.md) / ChatGLM3 6B 128K
# ChatGLM3 6B 128K
Zhipu AI (THUDM) · released 2024-01
**score 34.9** ±3.8 · #629 of 732 · from 3 benchmark results across 2 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 128K among every model on that task or test, worst to best.
other tests (3)
test result vs the others in its run difficulty counts why
RULER 64k 56.0% ▁●·▁·▂▃▄█▂·▁ beats 1/22 easy · avg 82% 51% at its level
Open PT-LLM Leaderboard average (… 49.0% ▃▁▁▁▁▁▁●▂█▅▁ beats 66/276 medium · avg 72% 30% at its level · noisy
RULER 128k 42.0% ▄··●▄▂▆██▄▄▂ beats 2/21 easy · avg 67% 12% at its level · noisy
source results share of the score
NVIDIA RULER repository README results … 2 63% ▇▇▇▇▇▇▇▇
Open Portuguese LLM Leaderboard 1 30% ▇▇▇▇
prior: pull toward the middle 7% ▇
weighted average = 34.9
Licences: NVIDIA RULER repository README results table ([Apache-2.0](https://github.com/NVIDIA/RULER/blob/c3f5e3b4f87f97e048793bb510a3a6b19a46bf3a/LICENSE)) · 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 128K (Q4_K_M, needs ~4.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/chatglm3-6b-128k-GGUF/tree/89d663885d5a1b591a2c5cb523f8204d1824a14f))
```sh
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)
```sh
llama serve -hf mradermacher/chatglm3-6b-128k-GGUF:Q4_K_M
```
Other versions: Q8_0 (~6.6 GB) `llama cli -hf mradermacher/chatglm3-6b-128k-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 128K (Q4_K_M, needs ~4.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/chatglm3-6b-128k-GGUF/tree/89d663885d5a1b591a2c5cb523f8204d1824a14f))
```sh
lms get https://huggingface.co/mradermacher/chatglm3-6b-128k-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-128k-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 128K (Q4_K_M, needs ~4.1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/chatglm3-6b-128k-GGUF/tree/89d663885d5a1b591a2c5cb523f8204d1824a14f))
```sh
ollama run hf.co/mradermacher/chatglm3-6b-128k-GGUF:Q4_K_M
```
Other versions: Q8_0 (~6.6 GB) `ollama run hf.co/mradermacher/chatglm3-6b-128k-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (fp16, needs ~12.49 GB, from [huggingface.co/zai-org](https://huggingface.co/zai-org/chatglm3-6b-128k/tree/6381a643d74e3e6a1763580bb643e488dd278b98))
```sh
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'])"
```
Model repository and documentation: [huggingface.co/zai-org](https://huggingface.co/zai-org/chatglm3-6b-128k)
[interactive version](https://whatmodel.sh/model/chatglm3-6b-128k) · [JSON](https://whatmodel.sh/api?format=json&all=1)