whatmodel.sh / Falcon-180B-Chat

Falcon-180B-Chat

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Falcon-180B-Chat is an open-weights language model from TII, released September 2023. Its published size is 180B parameters and a 2k-token context window. Licence: falcon-180b-tii-license.

Catalogue identity notes

Arena entry 'falcon-180b-chat' (text). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'falcon-180b-chat' as 'falcon-180b-chat' (TII, Falcon-180B TII License), linking https://huggingface.co/tiiuae/falcon-180B-chat. Maker repo tiiuae/falcon-180B-chat @6882299: licence tag unknown. Parameters: nominal size from the model name (safetensors metadata 179.5B). Released 2023-09: month the maker's Hugging Face repo was created (createdAt 2023-09-04). Gated repo under the Falcon-180B TII License.

38.2±1.8#558 of 719 LLMsfrom 4 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of Falcon-180B-Chat, out of 828 with a score, then more from TII.

#modelscorewhereRAMreleased
648Llama 3.2 3B Instruct Meta38±1api $0.12/M +local~3.4GB2024-09
649Qwen-14B-Chat Alibaba38±1local~11GB2023-09
650Falcon-180B-Chat TII38±2local~115GB2023-09
651CodeGemma 7B (v1.0) Google38±1local~7.1GB2024-03
652Code Llama 13B Python Meta38±1local~9.8GB2023-08
more from TII
233Falcon-H1R 7B TII60±1local~6GB2026-01
332Falcon-H1 34B Instruct TII54±3local~23GB2025-05
494Falcon-H1 7B Instruct TII46±3local~6GB2025-05

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #353 of 393 * · 58% of its score · 2 tests

Beats Gemma 4 E2B, Code Llama 70B Instruct, Phi-3-mini-4k-instruct (June 2024 update) here, though they rank higher overall. Trails Gemma 3 270M IT, SmolLM2 360M Instruct, Granite 4.0 H 350M here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Polyrating rating (bias-aware re-fit of Chatbot Arena votes, mid-2024)1056.0 ✎▂▁▂▁▃▇█▆▇▄▂▂ beats 28/95avg 1201▇▇▇▇▇▇▇▇▇▇▇▇ 29%blunt
LMArena (text)1148.1 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 37/379avg 1268▇▇▇▇▇▇▇▇▇▇▇▇ 29%blunt
other tests2 not in a task · 40% of its score
testresultvs the others in its rundifficultycountswhy
Factual knowledge recall, Premium2k exact match (zero-shot)40.0% ✎▅····▃▅·██·█ beats 13/13medium · avg 59%▇▇▇▇▇▇▇▇▇▇ 24%at its level
Factual knowledge recall, Premium2k exact match (10-shot)49.3% ✎▃▅▃··▅▃·█▃▃▅ beats 12/13easy · avg 67%▇▇▇▇▇▇ 16%at its level

$ where to run it

Ollama · recommended

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Falcon-180B-Chat · needs ~108.5 GB · from ollama.com
    chat in the terminal
    ollama run falcon:180b-chat-q4_0

Official weights and documentation: huggingface.co/tiiuae ↗

$ how the score was worked out · Towards a Holistic Evaluation… 40%, Polyrating: A Cost-Effective… 29%, LMArena leaderboard dataset 29%

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
Towards a Holistic Evaluation of LLMs on Factual Knowledge Recall (arXiv:2404.16164v1)2▇▇▇▇▇▇▇▇▇▇▇▇ 40%CC-BY-4.0
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation (arXiv:2409.00696v3)1▇▇▇▇▇▇▇▇▇ 29%CC-BY-4.0
LMArena leaderboard dataset1▇▇▇▇▇▇▇▇▇ 29%CC BY 4.0
prior · pull toward the middle▇ 1%
weighted average= 38.2

$ 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