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InternLM2.5 7B Chat

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InternLM2.5 7B Chat is an open-weights language model from Shanghai AI Laboratory (InternLM), released June 2024. Its published size is 7.74B parameters. Licence: other.

Catalogue identity notes

Maker repo internlm/internlm2_5-7b-chat @eb72b54: licence tag other. 7,737,708,544 parameters from the Hugging Face safetensors metadata. Dense, so active = total. Released 2024-06: month the maker's Hugging Face repo was created (createdAt 2024-06-27). Ollama tags internlm2:7b-chat-v2.5-q4_K_M, internlm2:7b-chat-v2.5-q8_0 are the library's instruct/chat builds of this checkpoint (tag names on the pinned tags page); download sizes are estimated from the parameter count.

48.5±1.7#373 of 719 LLMsfrom 7 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of InternLM2.5 7B Chat, out of 828 with a score, then more from Shanghai AI Laboratory (InternLM).

#modelscorewhereRAMreleased
444Qwen2-Math 7B Instruct Alibaba49±2local~6GB2024-08
445Magicoder-S-DS 6.7B UIUC (ise-uiuc)49±1local~5.8GB2023-12
446InternLM2.5 7B Chat Shanghai AI Laboratory (InternLM)49±2local~6.1GB2024-06
447Hermes 3 Llama 3.1 70B Nous Research49±1local~44GB2024-07
448Qwen2.5 7B Instruct Alibaba48±1api $0.30/M +local~6GB2024-09
more from Shanghai AI Laboratory (InternLM)
486InternLM2.5 7B Chat 1M Shanghai AI Laboratory (InternLM)47±4local~6.4GB2024-06
520InternLM2.5 20B Chat Shanghai AI Laboratory (InternLM)46±2local~14GB2024-08
609InternLM2 Chat 20B Shanghai AI Laboratory (InternLM)41±1local~14GB2024-01

see all LLMs · find the ones that fit your machine

$ what it's good at

math▁▂▃▅▆▅▄█▄▁▁▁ #292 of 398 * · 33% of its score · 2 tests

Beats GPT-4 (0613), Athene-70B, Gemma 2 27B IT here, though they rank higher overall. Trails Gemma 7B (base), DeepSeek R1 Distill Qwen 1.5B, Phi-2 (2.7B) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
GSM8K lab86.0% ✎▂▂▂▅▅▆▅▇█▇▄▃ beats 109/146very easy · avg 89%▇▇▇▇▇▇▇▇ 19%sharp
MATH (subset unspecified) lab60.1% ✎▂▄▄▃█▇▅▆▄▃▄▁ beats 34/80medium · avg 60%▇▇▇▇▇▇ 14%at its level
other tests5 not in a task · 65% of its score
testresultvs the others in its rundifficultycountswhy
BIG-Bench Hard lab71.6% ✎▁▁▁▁▂▄▅▅█▃▄▁ beats 96/129easy · avg 75%▇▇▇▇▇▇▇▇▇ 22%sharp · at its level · small run
MMLU lab72.8% ✎▁▁▁▁▁▂▃▅█▃▆▃ beats 123/183easy · avg 78%▇▇▇▇▇▇▇ 17%at its level
GPQA (subset unspecified) lab38.4% ✎▂▁▂▂▄▃▆▃▅█▄▃ beats 35/104hard · avg 40%▇▇▇▇▇▇ 15%sharp
Open PT-LLM Leaderboard average (9 Portuguese tasks)69.2% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 159/276medium · avg 73%▇▇ 5.7%at its level · noisy
CMMLU lab78.0% ✎▅·▅····█▃▅█▅ beats 5/14easy · avg 75%▇▇ 5.2%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 InternLM2.5 7B Chat · needs ~4.7 GB · from huggingface.co/bartowski
    chat in the terminal
    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)
    llama serve -hf bartowski/internlm2_5-7b-chat-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 InternLM2.5 7B Chat · needs ~4.7 GB · from huggingface.co/bartowski

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/bartowski/internlm2_5-7b-chat-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 InternLM2.5 7B Chat · needs ~5.14 GB · from ollama.com
    chat in the terminal
    ollama run internlm2:7b-chat-v2.5-q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run InternLM2.5 7B Chat · needs ~4.4 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/internlm2_5-7b-chat-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/internlm2_5-7b-chat-4bit

Official weights and documentation: huggingface.co/internlm ↗

$ how the score was worked out · InternLM GitHub repository mo… 93%, Open Portuguese LLM Leaderboa… 6%

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
InternLM GitHub repository model cards (Shanghai AI Laboratory) · lab-reported6▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇▇ 6%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 48.5

$ 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