[whatmodel.sh](../../README.md) / InternLM2.5 7B Chat
# InternLM2.5 7B Chat
Shanghai AI Laboratory (InternLM) · released 2024-06 · 7.74B params · other
**score 49.6** ±1.6 · #381 of 732 · from 7 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 InternLM2.5 7B Chat among every model on that task or test, worst to best.
task vs the others rank counts tests
math ▁▁▃▅●▆▄█▄▂▁▁ #329 of 431* 37% 2
math · #329 of 431 · 37% of its score
Beats [GPT-4 (0613)](gpt-4-0613.md), [Athene-70B](athene-70b.md), [Gemma 2 27B IT](gemma-2-27b.md) here, though they rank higher overall. Trails [Stable Code 3B](stable-code-3b.md), [Qwen3 0.6B](qwen3-0-6b.md), [Falcon 40B (base)](falcon-40b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 86.0% ▂▂▂▂▃▄▅▇█●▆▄ beats 158/206 very easy · avg 88% 23% sharp
MATH (subset unspecified) (maker) 60.1% ▂▄▄▃█●▅▆▄▃▄▁ beats 34/80 medium · avg 56% 14% at its level
other tests (5)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 71.6% ▁▁▁▁▂▄▅▆●▃▃▁ beats 121/155 medium · avg 72% 20% sharp · at its level · small run
MMLU (maker) 72.8% ▁▁▁▁▁▂▂▅█●▆▄ beats 149/214 easy · avg 77% 18% at its level
GPQA (subset unspecified) (maker) 38.4% ▂▁▂▂▄▃●▃▅█▃▃ beats 35/106 hard · avg 38% 14% sharp
Open PT-LLM Leaderboard average (… 69.2% ▃▁▁▁▁▁▁▁▂●▅▁ beats 159/276 medium · avg 72% 6.2% at its level · noisy
CMMLU (maker) 78.0% ▅·▅····●▃▅█▅ beats 5/14 medium · avg 73% 4.4% at its level · noisy
source results share of the score
InternLM GitHub repository model cards … 6 93% ▇▇▇▇▇▇▇▇▇▇▇
Open Portuguese LLM Leaderboard 1 6% ▇
prior: pull toward the middle 1% ▇
weighted average = 49.6
Licences: InternLM GitHub repository model cards ([Apache-2.0](https://github.com/InternLM/InternLM/blob/68fdc71330059255fd647abf28219ad11e8e4865/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 InternLM2.5 7B Chat (Q4_K_M, needs ~4.7 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/internlm2_5-7b-chat-GGUF/tree/34fdc9783472bb1fd7d1d04a412d1f2ae029459a))
```sh
llama cli -hf bartowski/internlm2_5-7b-chat-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/internlm2_5-7b-chat-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.2 GB) `llama cli -hf bartowski/internlm2_5-7b-chat-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 InternLM2.5 7B Chat (Q4_K_M, needs ~4.7 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/internlm2_5-7b-chat-GGUF/tree/34fdc9783472bb1fd7d1d04a412d1f2ae029459a))
```sh
lms get https://huggingface.co/bartowski/internlm2_5-7b-chat-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 (~8.2 GB) `lms get https://huggingface.co/bartowski/internlm2_5-7b-chat-GGUF@Q8_0 -y` · 4bit (~4.4 GB, Mac only) `lms get https://huggingface.co/mlx-community/internlm2_5-7b-chat-4bit -y` · 8bit (~8.2 GB, Mac only) `lms get https://huggingface.co/mlx-community/internlm2_5-7b-chat-8bit -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 InternLM2.5 7B Chat (Q4_K_M, needs ~5.14 GB, from [ollama.com](https://ollama.com/library/internlm2/tags))
```sh
ollama run internlm2:7b-chat-v2.5-q4_K_M
```
Other versions: Q8_0 (~9.79 GB) `ollama run internlm2:7b-chat-v2.5-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run InternLM2.5 7B Chat (4bit, needs ~4.4 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/internlm2_5-7b-chat-4bit/tree/d12097a867721978142a6048399f470a3d18beee))
```sh
mlx_lm.chat --model mlx-community/internlm2_5-7b-chat-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/internlm2_5-7b-chat-4bit
```
Other versions: 8bit (~8.2 GB, Mac only) `mlx_lm.chat --model mlx-community/internlm2_5-7b-chat-8bit`
Model repository and documentation: [huggingface.co/internlm](https://huggingface.co/internlm/internlm2_5-7b-chat)
[interactive version](https://whatmodel.sh/model/internlm2-5-7b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)