[whatmodel.sh](../../README.md) / Falcon3 3B Instruct
# Falcon3 3B Instruct
TII · released 2024-12 · 3.23B params · falcon-llm
**score 38.4** ±2.8 · #589 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 3B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁●▃▆█▄▅▅▄▄▂▁ #306 of 313* 40% 2
coding ▁▁▁▃●▇█▇▅▄▃▁ #384 of 469 38% 4
agent tasks · #306 of 313 · 40% of its score
Beats [QwQ-32B-Preview](qwq-32b-preview.md), [DeepSeek-Coder-V2 Lite Instruct (16B-A2.4B)](deepseek-coder-v2-lite-instruct.md), [Phi-4-mini 3.8B](phi-4-mini.md) here, though they rank higher overall. Trails [Granite 4.0 350M](granite-4-0-350m.md), [Granite 4.0 H 350M](granite-4-0-h-350m.md), [Qwen2.5 0.5B Instruct](qwen2-5-0-5b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL V4 (December 2025 archive) 16.3% ▁▁●▂██▅▅▆▂▂▂ beats 3/77 hard · avg 31% 23% typical
BFCL v3 32.5% ▁▁▁●▁▃▅█▅▄▁▁ beats 8/114 medium · avg 51% 17% blunt · at its level
coding · #384 of 469 · 38% of its score
Beats [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Tulu 2 70B](tulu-2-70b.md) here, though they rank higher overall. Trails [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md), [WizardLM 13B V1.2](wizardlm-13b-v1-2.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CodeReviewQA: solution identifica… 19.5% ▃··▁▂▂●▂█▃▄▂ beats 14/47 medium · avg 51% 15% sharp · noisy
CodeReviewQA: automated code refi… 14.2% ▁▁▁▁▁▁●▂▃█▅▄ beats 8/47 hard · avg 32% 13% typical
CodeReviewQA: change localisation… 1.7% █▂●▁▂▂▂▃▅▅▅▇ beats 11/47 hard · avg 33% 10% sharp · too hard for it · noisy
CodeReviewQA: change type recogni… 52.0% ▁▁▁······●▃█ beats 5/47 easy · avg 67% 0.8% blunt · at its level · noisy
other tests (1)
test result vs the others in its run difficulty counts why
Open PT-LLM Leaderboard average (… 64.1% ▃▁▁▁▁▁▁▁▂●▅▁ beats 107/276 medium · avg 72% 18% at its level · noisy
source results share of the score
Berkeley Function Calling Leaderboard r… 2 40% ▇▇▇▇▇
CodeReviewQA: The Code Review Comprehen… 4 38% ▇▇▇▇▇
Open Portuguese LLM Leaderboard 1 18% ▇▇
prior: pull toward the middle 4% ▇
weighted average = 38.4
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 3B Instruct (Q4_K_M, needs ~2 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon3-3B-Instruct-GGUF/tree/142f28a7f0ec90e701157ec3c4e9842e9471dfcd))
```sh
llama cli -hf tiiuae/Falcon3-3B-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/Falcon3-3B-Instruct-GGUF:q4_k_m
```
Other versions: Q8_0 (~3.4 GB) `llama cli -hf tiiuae/Falcon3-3B-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 3B Instruct (Q4_K_M, needs ~2 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon3-3B-Instruct-GGUF/tree/142f28a7f0ec90e701157ec3c4e9842e9471dfcd))
```sh
lms get https://huggingface.co/tiiuae/Falcon3-3B-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 (~3.4 GB) `lms get https://huggingface.co/tiiuae/Falcon3-3B-Instruct-GGUF@q8_0 -y` · 4bit (~1.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/Falcon3-3B-Instruct-4bit -y` · 8bit (~3.4 GB, Mac only) `lms get https://huggingface.co/mlx-community/Falcon3-3B-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 3B Instruct (Q4_K_M, needs ~2.44 GB, from [ollama.com](https://ollama.com/library/falcon3/tags))
```sh
ollama run falcon3:3b-instruct-q4_K_M
```
Other versions: Q8_0 (~4.38 GB) `ollama run falcon3:3b-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 3B Instruct (4bit, needs ~1.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Falcon3-3B-Instruct-4bit/tree/164db76e71483f19d893034c3a68ae5334fcbff4))
```sh
mlx_lm.chat --model mlx-community/Falcon3-3B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Falcon3-3B-Instruct-4bit
```
Other versions: 8bit (~3.4 GB, Mac only) `mlx_lm.chat --model mlx-community/Falcon3-3B-Instruct-8bit`
Model repository and documentation: [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon3-3B-Instruct)
[interactive version](https://whatmodel.sh/model/falcon3-3b) · [JSON](https://whatmodel.sh/api?format=json&all=1)