[whatmodel.sh](../../README.md) / Falcon2 11B (base) # Falcon2 11B (base) TII · released 2024-05 · 11.1B params · falcon-llm **score 43.2** ±3.0 · #514 of 732 · from 3 benchmark results across 3 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Falcon2 11B (base) 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
Polish EQ-Bench v2 (SpeakLeash)      42.4%  ▁··▁▁▂▂●▅▆█▆ beats 12/70    medium · avg 62%     59%  at its level
Open PT-LLM Leaderboard average (…   64.2%  ▃▁▁▁▁▁▁▁▂●▅▁ beats 108/276  medium · avg 72%     22%  at its level · noisy
EuroEval Life in the UK (English …   73.7%  ▁▁▁▁▁▁▁▂●█▄▃ beats 46/146   easy · avg 82%       15%  at its level · 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
SpeakLeash Polish EQ-Bench leaderboard        1   59% ▇▇▇▇▇▇▇
Open Portuguese LLM Leaderboard               1   22% ▇▇▇
EuroEval leaderboards                         1   15% ▇▇
prior: pull toward the middle                      4% ▇
weighted average                                 = 43.2

_{Licences: SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE))} ## 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 Falcon2 11B (base) (Q4_K_M, needs ~6.8 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/falcon-11B-GGUF/tree/597dbbc99a829d5e7221858ac60eca46b991f0cb)) ```sh llama cli -hf bartowski/falcon-11B-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf bartowski/falcon-11B-GGUF:Q4_K_M ``` Other versions: Q8_0 (~11.8 GB) `llama cli -hf bartowski/falcon-11B-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 Falcon2 11B (base) (Q4_K_M, needs ~6.8 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/falcon-11B-GGUF/tree/597dbbc99a829d5e7221858ac60eca46b991f0cb)) ```sh lms get https://huggingface.co/bartowski/falcon-11B-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 (~11.8 GB) `lms get https://huggingface.co/bartowski/falcon-11B-GGUF@Q8_0 -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 Falcon2 11B (base) (Q4_0, needs ~6.4 GB, from [ollama.com](https://ollama.com/library/falcon2/tags)) ```sh ollama run falcon2:11b ``` Model repository and documentation: [huggingface.co/tiiuae](https://huggingface.co/tiiuae/falcon-11B) [interactive version](https://whatmodel.sh/model/falcon2-11b) · [JSON](https://whatmodel.sh/api?format=json&all=1)