tiiuae/Falcon-H1-34B-Instruct-GGUF

Table of Contents

0. TL;DR
1. Model Details
2. Training Details
3. Usage
4. Evaluation
5. Citation

TL;DR

Model Details

Model Description

• Developed by: https://www.tii.ae
• Model type: Causal decoder-only
• Architecture: Hybrid Transformers + Mamba architecture
• Language(s) (NLP): English, Multilingual
• License: Falcon-LLM License

Training details

For more details about the training protocol of this model, please refer to the Falcon-H1 technical blogpost.

Usage

Currently to use this model you can either rely on Hugging Face transformers, vLLM or our custom fork of llama.cpp library.

Inference

Make sure to install the latest version of transformers or vllm, eventually install these packages from source:

pip install git+https://github.com/huggingface/transformers.git

Refer to the official vLLM documentation for more details on building vLLM from source.

🤗 transformers

Refer to the snippet below to run H1 models using 🤗 transformers:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "tiiuae/Falcon-H1-1B-Base"

model = AutoModelForCausalLM.from_pretrained(
  model_id,
  torch_dtype=torch.bfloat16,
  device_map="auto"
)

# Perform text generation

vLLM

For vLLM, simply start a server by executing the command below:

# pip install vllm
vllm serve tiiuae/Falcon-H1-1B-Instruct --tensor-parallel-size 2 --data-parallel-size 1

🦙 llama.cpp

While we are working on integrating our architecture directly into llama.cpp library, you can install our fork of the library and use it directly: https://github.com/tiiuae/llama.cpp-Falcon-H1 Use the same installing guidelines as llama.cpp.

Evaluation

Falcon-H1 series perform very well on a variety of tasks, including reasoning tasks.

Tasks	Falcon-H1-34B	Qwen3-32B	Qwen2.5-72B	Qwen2.5-32B	Gemma3-27B	Llama3.3-70B	Llama4-scout
General
BBH	70.68	62.47	72.52	68.72	67.28	69.15	64.9
ARC-C	61.01	48.98	46.59	44.54	54.52	63.65	56.14
TruthfulQA	65.27	58.58	69.8	70.28	64.26	66.15	62.74
HellaSwag	81.94	68.89	68.79	73.95	57.25	70.24	65.03
MMLU	84.05	80.89	84.42	82.8	78.01	82.08	80.4
Math
GSM8k	83.62	88.78	82.26	78.47	90.37	93.71	90.37
MATH-500	83.8	82.0	83.6	82.2	90.0	70.6	83.2
AMC-23	69.38	67.34	67.34	68.75	77.81	39.38	69.06
AIME-24	23.75	27.71	17.29	17.92	27.5	12.92	27.92
AIME-25	16.67	19.79	15.21	11.46	22.71	1.25	8.96
Science
GPQA	41.53	30.2	37.67	34.31	36.49	31.99	31.8
GPQA_Diamond	49.66	49.49	44.95	40.74	47.47	42.09	51.18
MMLU-Pro	58.73	54.68	56.35	56.63	47.81	53.29	55.58
MMLU-stem	83.57	81.64	82.59	82.37	73.55	74.88	75.2
Code
HumanEval	87.2	90.85	87.2	90.24	86.59	83.53	85.4
HumanEval+	81.71	85.37	80.49	82.32	78.05	79.87	78.7
MBPP	83.86	86.24	89.68	87.83	88.36	88.09	81.5
MBPP+	71.43	71.96	75.4	74.07	74.07	73.81	64.8
LiveCodeBench	49.71	45.01	54.6	49.12	39.53	40.31	40.12
CRUXEval	73.07	78.45	75.63	73.5	74.82	69.53	68.32
Instruction Following
IFEval	89.37	86.97	86.35	81.79	83.19	89.94	86.32
Alpaca-Eval	48.32	64.21	49.29	39.26	56.16	38.27	36.26
MTBench	9.2	9.05	9.16	9.09	8.75	8.98	8.98
LiveBench	46.26	63.05	54.03	52.92	55.41	53.11	54.21

You can check more in detail on our our release blogpost, detailed benchmarks.

Useful links

• View our release blogpost.
• Feel free to join our discord server if you have any questions or to interact with our researchers and developers.

Citation

If the Falcon-H1 family of models were helpful to your work, feel free to give us a cite.

@misc{tiifalconh1,
    title = {Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance},
    url = {https://falcon-lm.github.io/blog/falcon-h1},
    author = {Falcon-LLM Team},
    month = {May},
    year = {2025}
}