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tiiuae / Falcon-H1-34B-Instruct-GGUF
Model's Last Updated: August 04 2025
text-generation
Introduction of Falcon-H1-34B-Instruct-GGUF
Model Details of Falcon-H1-34B-Instruct-GGUF
Table of Contents
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}
}
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