Falcon H1 34B Instruct GPTQ Int8

🚀 Falcon-H1 Transformer Library

The Falcon-H1 series of models, developed by TII, offers high - performance language processing capabilities with a hybrid architecture, suitable for a wide range of NLP tasks.

🚀 Quick Start

To start using the Falcon - H1 models, you can choose from Hugging Face transformers, vLLM, or our custom fork of the llama.cpp library.

✨ Features

• Hybrid Architecture: Combines Transformers and Mamba architecture for enhanced performance.
• Multilingual Support: Supports English and other languages, suitable for diverse NLP tasks.
• Good Performance: Performs well on various tasks, including reasoning tasks.

📦 Installation

Install transformers

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

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

💻 Usage Examples

Basic Usage

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

Using vLLM

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

Using llama.cpp

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.

📚 Documentation

Model Details

Training Details

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

Evaluation

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

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.

📄 License

This model is licensed under the Falcon - LLM License.

📖 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}
}