whatmodel.sh / RedPajama-INCITE Base 3B v1

RedPajama-INCITE Base 3B v1

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RedPajama-INCITE Base 3B v1 is an open-weights language model from Together, released May 2023. Its published size is 2.8B parameters and a 2k-token context window. Licence: apache-2.0.

Catalogue identity notes

Base model (not -Chat/-Instruct).

24.9±2.7#673 of 719 LLMsfrom 5 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of RedPajama-INCITE Base 3B v1, out of 828 with a score, then more from Together.

#modelscorewhereRAMreleased
776Qwen2 0.5B Instruct Alibaba25±2local~1.8GB2024-06
777Pythia 12B EleutherAI25±1local~9.5GB2023-02
778RedPajama-INCITE Base 3B v1 Together25±3local~3.4GB2023-05
779ChatGLM2-6B Zhipu AI (THUDM)25±2local~15GB2023-06
780GPT-J 6B EleutherAI25±1local~14GB2021-06
more from Together
761RedPajama-INCITE 7B Base Together27±1local~16GB2023-06

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests5 not in a task · 97% of its score
testresultvs the others in its rundifficultycountswhy
WinoGrande lab63.2% ✎▁···▂▁▁▅█▇▃▁ beats 34/87easy · avg 88%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 39%small run
HellaSwag lab63.2% ✎▁▁▁▁▂▂▃▇█▆▅▃ beats 28/85very easy · avg 90%▇▇▇▇▇▇▇▇▇▇▇ 26%at its level · small run
LAMBADA (OpenAI version), last-word accuracy lab65.4% ✎▁··▁▁▂▂▂▃▅█▅ beats 24/49very easy · avg 89%▇▇▇▇▇ 13%blunt · at its level · small run
PIQA lab74.7% ✎▁▁▁▃▃▄▇█▄▃▁▁ beats 34/83easy · avg 88%▇▇▇▇ 10%blunt · at its level · small run
Open PT-LLM Leaderboard average (9 Portuguese tasks)29.7% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 3/276medium · avg 73%▇▇▇ 8.0%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 RedPajama-INCITE Base 3B v1 · needs ~1.8 GB · from huggingface.co/kikimuncher2
    chat in the terminal
    llama cli -hf kikimuncher2/RedPajama-INCITE-Base-3B-v1-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 kikimuncher2/RedPajama-INCITE-Base-3B-v1-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 RedPajama-INCITE Base 3B v1 · needs ~1.8 GB · from huggingface.co/kikimuncher2

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/kikimuncher2/RedPajama-INCITE-Base-3B-v1-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 RedPajama-INCITE Base 3B v1 · needs ~1.8 GB · from huggingface.co/kikimuncher2
    chat in the terminal
    ollama run hf.co/kikimuncher2/RedPajama-INCITE-Base-3B-v1-Q4_K_M-GGUF:q4_k_m

Other ways to run it

  1. Run it as published · needs ~5.69 GB · from huggingface.co/togethercomputer
    uv run --with torch --with 'transformers<5' python -c "from transformers import pipeline; print(pipeline('text-generation', model='togethercomputer/RedPajama-INCITE-Base-3B-v1')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Weights and documentation: huggingface.co/togethercomputer ↗

$ how the score was worked out · RedPajama — original zero-sho… 89%, Open Portuguese LLM Leaderboa… 8%

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
RedPajama — original zero-shot 3B comparison · lab-reported4▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 89%CC BY 4.0
Open Portuguese LLM Leaderboard (archived)1▇▇ 8%Apache-2.0
prior · pull toward the middle▇ 3%
weighted average= 24.9

$ 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