whatmodel.sh / Falcon-H1 1.5B Instruct

Falcon-H1 1.5B Instruct

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Falcon-H1 1.5B Instruct is an open-weights language model from TII, released May 2025. Its published size is 1.55B parameters and a 131k-token context window. Licence: other (falcon-llm-license).

Catalogue identity notes

Maker repo tiiuae/Falcon-H1-1.5B-Instruct @80ebc50: licence tag other (falcon-llm-license). 1,554,863,488 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2025-05: month the maker's Hugging Face repo was created (createdAt 2025-05-01).

31.3±4.1#632 of 719 LLMsfrom 3 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Falcon-H1 1.5B Instruct, out of 828 with a score, then more from TII.

#modelscorewhereRAMreleased
727Qwen-7B (base) Alibaba32±1local~6.6GB2023-08
728Phi-1.5 (1.3B) Microsoft32±1local~2.4GB2023-09
729Falcon-H1 1.5B Instruct TII31±4local~2.4GB2025-05
730Flan-T5 XL Google31±2local~14GB2022-10
731Qwen2 1.5B Instruct Alibaba31±1local~2.4GB2024-06
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

other tests3 not in a task · 92% of its score
testresultvs the others in its rundifficultycountswhy
HindiGen-v1 3C3H score (Inception, Claude 3.5 Sonnet judge)6.3% ✎▃▁▁▁·▂·█▆█▇▁ beats 0/31medium · avg 48%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 45%typical
Open PT-LLM Leaderboard average (9 Portuguese tasks)63.0% ✎▃▁▁▁▁▁▁▁▃█▅▁ beats 101/276medium · avg 73%▇▇▇▇▇▇▇▇▇▇ 26%noisy
AraGen-v3 3C3H score (Inception, Claude Sonnet 4 judge)4.6% ✎▁▁·▃▂▂▄█▄▅▄▄ beats 2/66hard · avg 31%▇▇▇▇▇▇▇▇▇ 22%too hard for it

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Falcon-H1 1.5B Instruct · needs ~0.9 GB · from huggingface.co/tiiuae (official)
    chat in the terminal
    llama cli -hf tiiuae/Falcon-H1-1.5B-Instruct-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf tiiuae/Falcon-H1-1.5B-Instruct-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Falcon-H1 1.5B Instruct · needs ~0.9 GB · from huggingface.co/tiiuae (official)

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/tiiuae/Falcon-H1-1.5B-Instruct-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Falcon-H1 1.5B Instruct · needs ~0.9 GB · from huggingface.co/tiiuae (official)
    chat in the terminal
    ollama run hf.co/tiiuae/Falcon-H1-1.5B-Instruct-GGUF:Q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Falcon-H1 1.5B Instruct · needs ~0.9 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/Falcon-H1-1.5B-Instruct-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/Falcon-H1-1.5B-Instruct-4bit

Other ways to run it

  1. Run it as published · needs ~3.11 GB · from huggingface.co/tiiuae (official)
    uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='tiiuae/Falcon-H1-1.5B-Instruct', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/tiiuae ↗

$ how the score was worked out · Inception Arabic/Hindi leader… 67%, Open Portuguese LLM Leaderboa… 26%

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
Inception Arabic/Hindi leaderboards (AraGen-v3, HindiGen-v1, Arabic IFEval)2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 67%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇▇▇▇▇▇▇▇ 26%Apache-2.0
prior · pull toward the middle▇▇ 8%
weighted average= 31.3

$ 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