[whatmodel.sh](../../README.md) / Falcon3 7B Instruct
# Falcon3 7B Instruct
TII · released 2024-11 · 7.46B params · falcon-llm
**score 45.3** ±2.4 · #471 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 7B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁●▆█▄▅▅▄▄▂▁ #286 of 313* 36% 2
coding ▁▁▁▃▅●█▇▅▄▃▁ #341 of 469 47% 4
agent tasks · #286 of 313 · 36% of its score
Beats [Magistral Small 1.0 (2506)](magistral-small-2506.md), [Bielik 11B v2.3 Instruct](bielik-11b-v2-3-instruct.md), [Mixtral 8x22B Instruct v0.1](mixtral-8x22b-instruct-v0-1.md) here, though they rank higher overall. Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md), [Qwen3 0.6B](qwen3-0-6b.md), [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL V4 (December 2025 archive) 24.0% ▁▁▁▂●█▅▅▆▂▂▂ beats 13/77 hard · avg 31% 22% typical
BFCL v3 43.6% ▁▁▁▁▁●▅█▅▄▁▁ beats 21/114 medium · avg 51% 14% blunt · at its level
coding · #341 of 469 · 47% of its score
Beats [Qwen3-VL 4B Instruct](qwen3-vl-4b-instruct.md), [LFM2.5 2.6B](lfm2-5-2-6b.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md) here, though they rank higher overall. Trails [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CodeReviewQA: solution identifica… 35.3% ▃··▁▂▂▃●█▃▄▂ beats 19/47 medium · avg 51% 18% sharp · at its level · noisy
CodeReviewQA: change localisation… 22.6% █▂▁▁▂▂▂●▅▅▅▇ beats 21/47 hard · avg 33% 15% sharp · noisy
CodeReviewQA: automated code refi… 14.3% ▁▁▁▁▁▁●▂▃█▅▄ beats 9/47 hard · avg 32% 13% typical
CodeReviewQA: change type recogni… 73.2% ▁▁▁······▁▃● beats 25/47 easy · avg 67% 0.6% blunt · at its level · noisy
other tests (1)
test result vs the others in its run difficulty counts why
Open PT-LLM Leaderboard average (… 72.8% ▃▁▁▁▁▁▁▁▂●▅▁ beats 200/276 medium · avg 72% 15% at its level · noisy
source results share of the score
CodeReviewQA: The Code Review Comprehen… 4 47% ▇▇▇▇▇▇
Berkeley Function Calling Leaderboard r… 2 36% ▇▇▇▇
Open Portuguese LLM Leaderboard 1 15% ▇▇
prior: pull toward the middle 3% ▇
weighted average = 45.3
Licences: CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · 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 7B Instruct (Q4_K_M, needs ~4.6 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon3-7B-Instruct-GGUF/tree/b5eb297a52350fa68f8b88be3ce16a57578ba114))
```sh
llama cli -hf tiiuae/Falcon3-7B-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/Falcon3-7B-Instruct-GGUF:q4_k_m
```
Other versions: Q8_0 (~7.9 GB) `llama cli -hf tiiuae/Falcon3-7B-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 7B Instruct (Q4_K_M, needs ~4.6 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon3-7B-Instruct-GGUF/tree/b5eb297a52350fa68f8b88be3ce16a57578ba114))
```sh
lms get https://huggingface.co/tiiuae/Falcon3-7B-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 (~7.9 GB) `lms get https://huggingface.co/tiiuae/Falcon3-7B-Instruct-GGUF@q8_0 -y` · 4bit (~4.2 GB, Mac only) `lms get https://huggingface.co/mlx-community/Falcon3-7B-Instruct-4bit -y` · 8bit (~7.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Falcon3-7B-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 7B Instruct (Q4_K_M, needs ~4.98 GB, from [ollama.com](https://ollama.com/library/falcon3/tags))
```sh
ollama run falcon3:7b-instruct-q4_K_M
```
Other versions: Q8_0 (~9.45 GB) `ollama run falcon3:7b-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 7B Instruct (4bit, needs ~4.2 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Falcon3-7B-Instruct-4bit/tree/009e488b528d27f21b428728dadfe7a1867f0cf8))
```sh
mlx_lm.chat --model mlx-community/Falcon3-7B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Falcon3-7B-Instruct-4bit
```
Other versions: 8bit (~7.9 GB, Mac only) `mlx_lm.chat --model mlx-community/Falcon3-7B-Instruct-8bit`
Model repository and documentation: [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon3-7B-Instruct)
[interactive version](https://whatmodel.sh/model/falcon3-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)