whatmodel.sh / Infinity-Instruct-3M-0625-Llama3-8B

Infinity-Instruct-3M-0625-Llama3-8B

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Infinity-Instruct-3M-0625-Llama3-8B is an open-weights language model from BAAI, released July 2024. Its published size is 8.03B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo BAAI/Infinity-Instruct-3M-0625-Llama3-8B @7be7c0f: licence tag apache-2.0. 8,030,261,248 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).

45.6±1.7#570 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-Llama3-8B, out of 1132 with a score, then more from BAAI.

#modelscorewhereRAMreleased
719LFM2 8B-A1B Liquid AI46±1comparison only2025-10
720OpenChat 3.5 0106 Gemma OpenChat46±3local~6.9GB2024-03
721Infinity-Instruct-3M-0625-Llama3-8B BAAI46±2local~6.6GB2024-07
722Olmo 3 7B Instruct Ai246±1local~5.8GB2025-11
723InternLM2.5 20B Chat Shanghai AI Laboratory (InternLM)46±2local~14GB2024-08
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▁▁▁▄▆▅▆█▆▆▆▂ #326 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-controlled27.5% ✎▁▂▂▃▆▆█▆▄▄▁▁ beats 74/122medium · avg 39%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%at its level
other tests1 not in a task · 5.8% of its score
testresultvs the others in its rundifficultycountswhy
Open PT-LLM Leaderboard average (9 Portuguese tasks)69.0% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 157/276medium · avg 73%▇▇ 5.8%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-0625-Llama3-8B · needs ~4.9 GB · from huggingface.co/featherless-ai-quants
    chat in the terminal
    llama cli -hf featherless-ai-quants/BAAI-Infinity-Instruct-3M-0625-Llama3-8B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf featherless-ai-quants/BAAI-Infinity-Instruct-3M-0625-Llama3-8B-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-0625-Llama3-8B · needs ~4.9 GB · from huggingface.co/featherless-ai-quants

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/featherless-ai-quants/BAAI-Infinity-Instruct-3M-0625-Llama3-8B-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-0625-Llama3-8B · needs ~4.9 GB · from huggingface.co/featherless-ai-quants
    chat in the terminal
    ollama run hf.co/featherless-ai-quants/BAAI-Infinity-Instruct-3M-0625-Llama3-8B-GGUF:Q4_K_M

Other ways to run it

  1. Run it as published · needs ~16.06 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-Llama3-8B', 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= 45.6

$ 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