whatmodel.sh / Infinity-Instruct-3M-0625-Yi-1.5-9B

Infinity-Instruct-3M-0625-Yi-1.5-9B

+ contribute
Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Infinity-Instruct-3M-0625-Yi-1.5-9B is an open-weights language model from BAAI, released July 2024. Its published size is 8.83B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo BAAI/Infinity-Instruct-3M-0625-Yi-1.5-9B @a42c86c: licence tag apache-2.0. 8,829,407,232 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-07: month the maker's Hugging Face repo was created (createdAt 2024-07-09).

42.5±1.8#658 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-0625-Yi-1.5-9B, out of 1132 with a score, then more from BAAI.

#modelscorewhereRAMreleased
815Phi-3-small-8k-instruct Microsoft43±2local~17GB2024-05
816CodeGemma 7B IT (v1.0) Google43±1local~7.1GB2024-03
817Infinity-Instruct-3M-0625-Yi-1.5-9B BAAI43±2local~7.1GB2024-07
818Aya Vision 32B Cohere42±3api price unknown +local~71GB2025-03
819Llama-3-Nanda-10B-Chat MBZUAI & Inception42±5local~43GB2024-10
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▁▁▁▄▆▅▆█▆▆▆▂ #361 of 466 * · 92% of its score · 1 test

Beats Gemma 4 E4B, OpenThinker3 7B, Gemma 4 E2B here, though they rank higher overall. Trails LFM2 350M, Gemma 3 1B, Flan-T5 XXL here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled20.5% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 53/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 92%typical
other tests1 not in a task · 6.7% of its score
testresultvs the others in its rundifficultycountswhy
Open PT-LLM Leaderboard average (9 Portuguese tasks)70.7% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 170/276medium · avg 73%▇▇▇ 6.7%at its level · noisy

$ where to run it

llama.cpp · recommended

  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-0625-Yi-1.5-9B · needs ~5.3 GB · from huggingface.co/Cran-May
    chat in the terminal
    llama cli -hf Cran-May/Infinity-Instruct-3M-0625-Yi-1.5-9B-Q4_K_M-GGUF:q4_k_m
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf Cran-May/Infinity-Instruct-3M-0625-Yi-1.5-9B-Q4_K_M-GGUF:q4_k_m

LM Studio

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Infinity-Instruct-3M-0625-Yi-1.5-9B · needs ~5.3 GB · from huggingface.co/Cran-May

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/Cran-May/Infinity-Instruct-3M-0625-Yi-1.5-9B-Q4_K_M-GGUF@q4_k_m -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Infinity-Instruct-3M-0625-Yi-1.5-9B · needs ~5.3 GB · from huggingface.co/Cran-May
    chat in the terminal
    ollama run hf.co/Cran-May/Infinity-Instruct-3M-0625-Yi-1.5-9B-Q4_K_M-GGUF:q4_k_m

Other ways to run it

  1. Run it as published · needs ~17.66 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-0625-Yi-1.5-9B', 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… 92%, Open Portuguese LLM Leaderboa… 7%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 92%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇▇ 7%Apache-2.0
prior · pull toward the middle▇ 2%
weighted average= 42.5

$ 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