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Cogito v1 Preview Llama 8B

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Cogito v1 Preview Llama 8B is an open-weights language model from Deep Cogito, released March 2025. Its published size is 8.03B parameters and a 128k-token context window. Licence: llama3.1.

Catalogue identity notes

Maker repo deepcogito/cogito-v1-preview-llama-8B @64c4236: licence tag llama3.1. 8,030,261,248 parameters from the Hugging Face safetensors metadata. Dense, so active = total. Released 2025-03: month the maker's Hugging Face repo was created (createdAt 2025-03-31). Ollama tags cogito:8b-v1-preview-llama-q4_K_M, cogito:8b-v1-preview-llama-q8_0 are the library's instruct/chat builds of this checkpoint (tag names on the pinned tags page); download sizes are estimated from the parameter count.

51.4±4.6#320 of 719 LLMsfrom 3 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Cogito v1 Preview Llama 8B, out of 828 with a score, then more from Deep Cogito.

#modelscorewhereRAMreleased
386Qwen2.5 Coder 7B Instruct Alibaba52±1local~6GB2024-09
387Qwen2.5 14B Instruct Alibaba52±1local~10GB2024-09
388Cogito v1 Preview Llama 8B Deep Cogito51±5local~6.2GB2025-03
389Hermes 3 Llama 3.1 405B Nous Research51±1local~114GB2024-08
390GPT-4 (0613) OpenAI51±1api $37.50/M2023-06
more from Deep Cogito
496Cogito v1 Preview Llama 3B Deep Cogito46±4local~3.4GB2025-03

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests3 not in a task · 91% of its score
testresultvs the others in its rundifficultycountswhy
Open PT-LLM Leaderboard average (9 Portuguese tasks)72.0% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 190/276medium · avg 73%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 39%at its level · noisy
EuroEval HellaSwag (English), test split, 5-shot generative MC, accuracy78.6% ✎▃·▁▁▂▁▂▅█▇▄▁ beats 68/100medium · avg 68%▇▇▇▇▇▇▇▇▇▇▇▇▇ 32%at its level · noisy
EuroEval Life in the UK (English knowledge MC), test split, 5-shot, accuracy81.9% ✎▁▁▁▁▁▁▁▂▄█▄▃ beats 76/146easy · avg 83%▇▇▇▇▇▇▇▇ 20%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 Cogito v1 Preview Llama 8B · needs ~4.9 GB · from huggingface.co/lmstudio-community
    chat in the terminal
    llama cli -hf lmstudio-community/cogito-v1-preview-llama-8B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf lmstudio-community/cogito-v1-preview-llama-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 Cogito v1 Preview Llama 8B · needs ~4.9 GB · from huggingface.co/lmstudio-community

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/lmstudio-community/cogito-v1-preview-llama-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 Cogito v1 Preview Llama 8B · needs ~5.32 GB · from ollama.com
    chat in the terminal
    ollama run cogito:8b-v1-preview-llama-q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Cogito v1 Preview Llama 8B · needs ~4.5 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/deepcogito-cogito-v1-preview-llama-8B-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/deepcogito-cogito-v1-preview-llama-8B-4bit

Official weights and documentation: huggingface.co/deepcogito ↗

$ how the score was worked out · EuroEval leaderboards (result… 51%, Open Portuguese LLM Leaderboa… 39%

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
EuroEval leaderboards (results collection), English generative leaderboard2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 51%MIT
Open Portuguese LLM Leaderboard (archived)1▇▇▇▇▇▇▇▇▇▇▇▇ 39%Apache-2.0
prior · pull toward the middle▇▇▇ 9%
weighted average= 51.4

$ 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