whatmodel.sh / InternLM2.5 1.8B Chat

InternLM2.5 1.8B Chat

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InternLM2.5 1.8B Chat is an open-weights language model from Shanghai AI Laboratory (InternLM), released July 2024. Its published size is 1.89B parameters and a 33k-token context window. Licence: other.

Catalogue identity notes

internlm/internlm2_5-1_8b-chat @57dcbc5 (created 2024-07-30, licence other). Independent row: Open PT-LLM (Portuguese) leaderboard average.

Model information sources

Release date ↗ · Weights licence ↗

HF safetensors metadata 1,889,110,016 parameters.

31.9±7.1#840 of 949 LLMsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of InternLM2.5 1.8B Chat, out of 1132 with a score, then more from Shanghai AI Laboratory (InternLM).

#	model	score	where	RAM	released
1009	Jais Family 590M Chat Inception (G42)	32±6	local	~4.7GB	2024-08
1010	Qwen1.5 4B Chat Alibaba	32±2	local	~4.1GB	2024-02
1011	InternLM2.5 1.8B Chat Shanghai AI Laboratory (InternLM)	32±7	local	~2.7GB	2024-07
1012	Ghost 7B Alpha Ghost X	32±2	local	~6.1GB	2024-04
1013	CodeGemma 2B (v1.0) Google	32±1	local	~3.2GB	2024-03
	more from Shanghai AI Laboratory (InternLM)
446	InternLM3 8B Instruct Shanghai AI Laboratory (InternLM)	54±11	local	~6.8GB	2025-01
621	InternLM2.5 7B Chat Shanghai AI Laboratory (InternLM)	49±2	local	~6.1GB	2024-06
678	InternLM2.5 7B Chat 1M Shanghai AI Laboratory (InternLM)	47±4	local	~6.4GB	2024-06

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests1 not in a task · 77% of its score

test	result	vs the others in its run	difficulty	counts	why
Open PT-LLM Leaderboard average (9 Portuguese tasks)	44.3% ✎	▃▁▁▁▁▁▃▁▂█▅▁ beats 56/276	medium · avg 73%	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 77%	at its level · noisy

$ where to run it

llama.cpp · recommended

version

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 ↗
Run InternLM2.5 1.8B Chat · needs ~1.2 GB · from huggingface.co/lmstudio-community
chat in the terminal
llama cli -hf lmstudio-community/internlm2_5-1_8b-chat-GGUF:Q4_K_M
Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf lmstudio-community/internlm2_5-1_8b-chat-GGUF:Q4_K_M

LM Studio

version

Install LM Studio · Open the app once before using the lms command.
download it (macOS / Windows / Linux) ↗
Run InternLM2.5 1.8B Chat · needs ~1.2 GB · from huggingface.co/lmstudio-community
open in LM Studio ↗
Or download it in the terminal
lms get https://huggingface.co/lmstudio-community/internlm2_5-1_8b-chat-GGUF@Q4_K_M -y
Then run lms ls to see its name, and lms chat followed by that name

Ollama

Install Ollama
download it (macOS / Windows) ↗
Linux
curl -fsSL https://ollama.com/install.sh | sh
Run InternLM2.5 1.8B Chat · needs ~1.1 GB · from ollama.com
chat in the terminal
ollama run internlm2:1.8b

Official weights and documentation: huggingface.co/internlm ↗

$ how the score was worked out · Open Portuguese LLM Leaderboa… 77%

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

source	results	share of the score	licence
Open Portuguese LLM Leaderboard (archived)	1	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 77%	Apache-2.0
prior · pull toward the middle		▇▇▇▇▇▇▇ 23%
weighted average		= 31.9

$ improve this page

Update model information Add a place to run it Add a test result Propose a benchmark Correct a benchmark score Edit 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 2026-10-11 02:20 UTC · sources and licences · json