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InternLM2.5 7B Chat 1M

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InternLM2.5 7B Chat 1M 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-1m @dfe8688: 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 2024-06-28).

46.6±4.3#408 of 719 LLMsfrom 3 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of InternLM2.5 7B Chat 1M, out of 828 with a score, then more from Shanghai AI Laboratory (InternLM).

#modelscorewhereRAMreleased
484Phind CodeLlama 34B v2 Phind47±1local~23GB2023-08
485CogVLM2 Llama3 Chinese Chat 19B Zhipu AI (THUDM)47±7comparison only2024-05
486InternLM2.5 7B Chat 1M Shanghai AI Laboratory (InternLM)47±4local~6.4GB2024-06
487EuroLLM 9B Instruct UTTER (EuroLLM)47±3local~6.9GB2024-11
488Granite 4.0 H Micro 3B IBM47±1local~3.4GB2025-09
more from Shanghai AI Laboratory (InternLM)
446InternLM2.5 7B Chat Shanghai AI Laboratory (InternLM)49±2local~6.1GB2024-06
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

other tests3 not in a task · 92% of its score
testresultvs the others in its rundifficultycountswhy
RULER 64k75.5% ✎▁▁·▁·▃▃▄█▂·▁ beats 3/22easy · avg 83%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 40%typical
Open PT-LLM Leaderboard average (9 Portuguese tasks)69.3% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 160/276medium · avg 73%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 37%at its level · noisy
RULER 128k68.9% ✎▄··▂▄▂▆██▄▄▂ beats 10/21easy · avg 68%▇▇▇▇▇▇ 15%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 1M · needs ~4.7 GB · from huggingface.co/bartowski
    chat in the terminal
    llama cli -hf bartowski/internlm2_5-7b-chat-1m-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-1m-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 1M · 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-1m-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 1M · needs ~4.7 GB · from huggingface.co/bartowski
    chat in the terminal
    ollama run hf.co/bartowski/internlm2_5-7b-chat-1m-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~15.48 GB · from huggingface.co/internlm (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='internlm/internlm2_5-7b-chat-1m', torch_dtype='auto', device_map='auto', trust_remote_code=True)('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/internlm ↗

$ how the score was worked out · NVIDIA RULER repository READM… 55%, Open Portuguese LLM Leaderboa… 37%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 55%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇▇▇▇▇▇▇▇▇▇▇ 37%Apache-2.0
prior · pull toward the middle▇▇ 8%
weighted average= 46.6

$ 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