[whatmodel.sh](../../README.md) / Falcon-180B-Chat # Falcon-180B-Chat TII · released 2023-09 · 180B params · falcon-180b-tii-license **score 40.1** ±1.8 · #565 of 732 · from 4 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 Falcon-180B-Chat among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
chat & writing  ▁▁▁▃●▄▆█▆▇▇▃   #360 of 402*     67%      2
chat & writing · #360 of 402 · 67% of its score Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [Phi-3-mini-4k-instruct (June 2024 update)](phi-3-mini-4k-instruct-june-2024.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Granite 4.0 H 350M](granite-4-0-h-350m.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty  counts  why
LMArena (text)                      1148.2  ▁▁▁▂●▃▃▆▆▇█▃ beats 37/385  avg 1260       35%  blunt
Polyrating rating (bias-aware re-…  1056.0  ▂▁▂▁▃●█▆▇▄▂▂ beats 28/95   avg 1187       32%  typical
other tests (2) 
test                                result  vs the others in its run  difficulty        counts  why
Factual knowledge recall, Premium…   40.0%  ▅····▃▅·██·● beats 13/13  medium · avg 52%     20%  at its level
Factual knowledge recall, Premium…   49.3%  ▃▅▃··▅▃·█▃▃● beats 12/13  medium · avg 61%     11%  at its level
## 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
LMArena leaderboard dataset                     1   35% ▇▇▇▇
Polyrating: A Cost-Effective and Bias-A…        1   32% ▇▇▇▇
Towards a Holistic Evaluation of LLMs o…        2   31% ▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 40.1
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Towards a Holistic Evaluation of LLMs on Factual Knowledge Recall ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) ## How to run it ### Ollama (recommended) 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Falcon-180B-Chat (Q4_0, needs ~108.5 GB, from [ollama.com](https://ollama.com/library/falcon/tags)) ```sh ollama run falcon:180b-chat-q4_0 ``` Model repository and documentation: [huggingface.co/tiiuae](https://huggingface.co/tiiuae/falcon-180B-chat) [interactive version](https://whatmodel.sh/model/falcon-180b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)