whatmodel.sh / Infinity-Instruct-3M-0613-Mistral-7B

Infinity-Instruct-3M-0613-Mistral-7B

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Infinity-Instruct-3M-0613-Mistral-7B is an open-weights language model from BAAI, released June 2024. Its published size is 7.24B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo BAAI/Infinity-Instruct-3M-0613-Mistral-7B @d8ffa6d: licence tag apache-2.0. 7,241,748,480 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-06: month the maker's Hugging Face repo was created (createdAt 2024-06-21).

44.7±1.7#592 of 949 LLMsfrom 2 benchmark results across 2 sources* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Infinity-Instruct-3M-0613-Mistral-7B, out of 1132 with a score, then more from BAAI.

#modelscorewhereRAMreleased
745Qwen2-Math 1.5B Instruct Alibaba45±2local~2.4GB2024-08
746Mistral Small (2402) Mistral45±1comparison only2024-02
747Infinity-Instruct-3M-0613-Mistral-7B BAAI45±2local~6.1GB2024-06
748Llama 3.1 8B Instruct Meta45±0api $0.22/M +local~6.2GB2024-07
749Command R Plus (04-2024) Cohere45±1local~43GB2024-04
more from BAAI
490Infinity-Instruct-7M-Gen-Llama3_1-70B BAAI53±2local~27GB2024-07
559Infinity-Instruct-7M-Gen-mistral-7B BAAI50±2local~6.1GB2024-07
581Infinity-Instruct-3M-0625-Llama3-70B BAAI50±2local~29GB2024-07

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #336 of 466 * · 93% of its score · 1 test

Beats Ministral 3 14B Reasoning 2512, Gemma 4 E4B, OpenThinker3 7B here, though they rank higher overall. Trails Flan-T5 XXL, LFM2 700M, EXAONE 3.5 2.4B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled25.5% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 67/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%at its level
other tests1 not in a task · 6.0% of its score
testresultvs the others in its rundifficultycountswhy
Open PT-LLM Leaderboard average (9 Portuguese tasks)70.1% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 165/276medium · avg 73%▇▇ 6.0%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 Infinity-Instruct-3M-0613-Mistral-7B · needs ~4.4 GB · from huggingface.co/mradermacher
    chat in the terminal
    llama cli -hf mradermacher/Infinity-Instruct-3M-0613-Mistral-7B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mradermacher/Infinity-Instruct-3M-0613-Mistral-7B-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 Infinity-Instruct-3M-0613-Mistral-7B · needs ~4.4 GB · from huggingface.co/mradermacher

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mradermacher/Infinity-Instruct-3M-0613-Mistral-7B-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 Infinity-Instruct-3M-0613-Mistral-7B · needs ~4.4 GB · from huggingface.co/mradermacher
    chat in the terminal
    ollama run hf.co/mradermacher/Infinity-Instruct-3M-0613-Mistral-7B-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~14.48 GB · from huggingface.co/BAAI (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='BAAI/Infinity-Instruct-3M-0613-Mistral-7B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/BAAI ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 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
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇▇ 6%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 44.7

$ 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