[whatmodel.sh](../../README.md) / Falcon3 1B Instruct # Falcon3 1B Instruct TII · released 2024-12 · 1.67B params · falcon-llm **score 26.4** ±3.8 · #681 of 732 · from 7 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 Falcon3 1B Instruct among every model on that task or test, worst to best. 
task         vs the others          rank  counts  tests
agent tasks  ●▁▃▆█▄▅▅▄▄▂▁   #312 of 313*     49%      2
coding       ▁▁▁●▅▇█▇▅▄▃▁    #445 of 469     24%      4
agent tasks · #312 of 313 · 49% of its score Beats [Gemma 3 1B](gemma-3-1b.md) here, though they rank higher overall. 
test                             result  vs the others in its run  difficulty        counts  why
BFCL v3                           19.7%  ●▁▁▁▁▃▅█▅▄▁▁ beats 1/114  medium · avg 51%     26%  blunt · at its level
BFCL V4 (December 2025 archive)   11.1%  ▁●▁▂██▅▅▆▂▂▂ beats 2/77   hard · avg 31%       23%  typical
coding · #445 of 469 · 24% of its score Beats [Qwen3.5 2B](qwen3-5-2b.md), [Molmo 7B-D (0924)](molmo-7b-d.md), [Tulu 2 7B](tulu-2-7b.md) here, though they rank higher overall. 
test                                result  vs the others in its run  difficulty        counts  why
CodeReviewQA: automated code refi…    3.4%  ▁●▁▁▁▁▂▂▃█▅▄ beats 1/47   hard · avg 32%       13%  too hard for it
CodeReviewQA: solution identifica…    0.0%  ●··▁▂▂▃▂█▃▄▂ beats 1/47   medium · avg 51%    6.7%  sharp · too hard for it · noisy
CodeReviewQA: change localisation…    0.0%  ●▂▁▁▂▂▂▃▅▅▅▇ beats 2/47   hard · avg 33%      3.5%  sharp · too hard for it · noisy
CodeReviewQA: change type recogni…   55.8%  ▁▁▁······●▃█ beats 7/47   easy · avg 67%      1.5%  blunt · at its level · noisy
other tests (1) 
test                                result  vs the others in its run   difficulty        counts  why
Open PT-LLM Leaderboard average (…   44.2%  ▃▁▁▁▁▁●▁▂█▅▁ beats 55/276  medium · avg 72%     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
Berkeley Function Calling Leaderboard r…        2   49% ▇▇▇▇▇▇
CodeReviewQA: The Code Review Comprehen…        4   24% ▇▇▇
Open Portuguese LLM Leaderboard                 1   20% ▇▇
prior: pull toward the middle                        7% ▇
weighted average                                   = 26.4
Licences: Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · 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 Falcon3 1B Instruct (Q4_K_M, needs ~1.1 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon3-1B-Instruct-GGUF/tree/fc404df7cfbd32eaefbcbeb2079aba684e894d39)) ```sh llama cli -hf tiiuae/Falcon3-1B-Instruct-GGUF:q4_k_m ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf tiiuae/Falcon3-1B-Instruct-GGUF:q4_k_m ``` Other versions: Q8_0 (~1.8 GB) `llama cli -hf tiiuae/Falcon3-1B-Instruct-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 Falcon3 1B Instruct (Q4_K_M, needs ~1.1 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon3-1B-Instruct-GGUF/tree/fc404df7cfbd32eaefbcbeb2079aba684e894d39)) ```sh lms get https://huggingface.co/tiiuae/Falcon3-1B-Instruct-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 (~1.8 GB) `lms get https://huggingface.co/tiiuae/Falcon3-1B-Instruct-GGUF@q8_0 -y` · 4bit (~0.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Falcon3-1B-Instruct-4bit -y` · 8bit (~1.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/Falcon3-1B-Instruct-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 Falcon3 1B Instruct (Q4_K_M, needs ~1.5 GB, from [ollama.com](https://ollama.com/library/falcon3/tags)) ```sh ollama run falcon3:1b-instruct-q4_K_M ``` Other versions: Q8_0 (~2.5 GB) `ollama run falcon3:1b-instruct-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 Falcon3 1B Instruct (4bit, needs ~0.9 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Falcon3-1B-Instruct-4bit/tree/56ed268fcb174dd7ce223eb86202460e2e5032ce)) ```sh mlx_lm.chat --model mlx-community/Falcon3-1B-Instruct-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/Falcon3-1B-Instruct-4bit ``` Other versions: 8bit (~1.8 GB, Mac only) `mlx_lm.chat --model mlx-community/Falcon3-1B-Instruct-8bit` Model repository and documentation: [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon3-1B-Instruct) [interactive version](https://whatmodel.sh/model/falcon3-1b) · [JSON](https://whatmodel.sh/api?format=json&all=1)