[whatmodel.sh](../../README.md) / Cogito v1 Preview Llama 8B # Cogito v1 Preview Llama 8B Deep Cogito · released 2025-03 · 8.03B params · llama3.1 **score 51.9** ±4.1 · #335 of 732 · from 3 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Cogito v1 Preview Llama 8B among every model on that task or test, worst to best.
other tests (3) 
test                                result  vs the others in its run    difficulty        counts  why
Open PT-LLM Leaderboard average (…   72.0%  ▃▁▁▁▁▁▁▁▂●▅▁ beats 190/276  medium · avg 72%     40%  at its level · noisy
EuroEval HellaSwag (English), tes…   78.6%  ▃·▁▁▂▁▂▅█●▄▁ beats 68/100   medium · avg 67%     33%  sharp · at its level · noisy
EuroEval Life in the UK (English …   81.9%  ▁▁▁▁▁▁▁▂▄●▄▃ beats 76/146   easy · avg 82%       20%  noisy
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md) 
source                           results  share of the score
EuroEval leaderboards                  2   53% ▇▇▇▇▇▇
Open Portuguese LLM Leaderboard        1   40% ▇▇▇▇▇
prior: pull toward the middle               8% ▇
weighted average                          = 51.9
Licences: EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) ## How to run it ### llama.cpp (recommended) 1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) ```sh # macOS / Linux curl -LsSf https://llama.app/install.sh | sh # Windows irm https://llama.app/install.ps1 | iex ``` 2. Run Cogito v1 Preview Llama 8B (Q4_K_M, needs ~4.9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/cogito-v1-preview-llama-8B-GGUF/tree/520d91e6aa70b38525af1258b5d410a96b9a3456)) ```sh 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) ```sh llama serve -hf lmstudio-community/cogito-v1-preview-llama-8B-GGUF:Q4_K_M ``` Other versions: Q8_0 (~8.5 GB) `llama cli -hf lmstudio-community/cogito-v1-preview-llama-8B-GGUF:Q8_0` ### LM Studio 1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download) 2. Run Cogito v1 Preview Llama 8B (Q4_K_M, needs ~4.9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/cogito-v1-preview-llama-8B-GGUF/tree/520d91e6aa70b38525af1258b5d410a96b9a3456)) ```sh lms get https://huggingface.co/lmstudio-community/cogito-v1-preview-llama-8B-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: Q8_0 (~8.5 GB) `lms get https://huggingface.co/lmstudio-community/cogito-v1-preview-llama-8B-GGUF@Q8_0 -y` · 4bit (~4.5 GB, Mac only) `lms get https://huggingface.co/mlx-community/deepcogito-cogito-v1-preview-llama-8B-4bit -y` · 8bit (~8.5 GB, Mac only) `lms get https://huggingface.co/mlx-community/deepcogito-cogito-v1-preview-llama-8B-8bit -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Cogito v1 Preview Llama 8B (Q4_K_M, needs ~5.32 GB, from [ollama.com](https://ollama.com/library/cogito/tags)) ```sh ollama run cogito:8b-v1-preview-llama-q4_K_M ``` Other versions: Q8_0 (~10.14 GB) `ollama run cogito:8b-v1-preview-llama-q8_0` ### MLX (Mac) 1. Install MLX (Mac). Apple silicon Macs only, text only. ```sh # with uv uv tool install mlx-lm # or pip install mlx-lm ``` 2. Run Cogito v1 Preview Llama 8B (4bit, needs ~4.5 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/deepcogito-cogito-v1-preview-llama-8B-4bit/tree/bad322ab4dd3e2f0d063607d9b409d6cf7ad8a9d)) ```sh 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 ```sh mlx_lm.server --model mlx-community/deepcogito-cogito-v1-preview-llama-8B-4bit ``` Other versions: 8bit (~8.5 GB, Mac only) `mlx_lm.chat --model mlx-community/deepcogito-cogito-v1-preview-llama-8B-8bit` Model repository and documentation: [huggingface.co/deepcogito](https://huggingface.co/deepcogito/cogito-v1-preview-llama-8B) [interactive version](https://whatmodel.sh/model/cogito-v1-preview-llama-8b) · [JSON](https://whatmodel.sh/api?format=json&all=1)