Falcon-E-1B-Base

Table of Contents

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

TL;DR

Model Details

Model Description

• Developed by: https://www.tii.ae
• Model type: Causal decoder-only / Base version
• Architecture: Pure-transformer - 1.58bit version
• Language(s) (NLP): English
• License: Falcon-LLM License

Training details

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

Usage

Currently to use this model you can either rely on Hugging Face transformers library or BitNet library. There are multiple ways to interact with the model depending on your target usage. For each of the Falcon-E series model, you have three variants: the BitNet model, the prequantized checkpoint for fine-tuning and the bfloat16 version of the BitNet model.

Inference

🤗 transformers

In case you want to perform inference on the BitNet checkpoint run:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

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

model = AutoModelForCausalLM.from_pretrained(
  model_id,
  torch_dtype=torch.bfloat16,
).to("cuda")

# Perform text generation

If you want to rather use the classic bfloat16 version, you can run:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "tiiuae/Falcon-E-1B-Base"
revision = "bfloat16"

model = AutoModelForCausalLM.from_pretrained(
  model_id,
  torch_dtype=torch.bfloat16,
  revision=revision,
).to("cuda")

# Perform text generation

BitNet

git clone https://github.com/microsoft/BitNet && cd BitNet
pip install -r requirements.txt
python setup_env.py --hf-repo tiiuae/Falcon-E-1B-Base -q i2_s
python run_inference.py -m models/Falcon-E-1B-Base/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv

mlx-lm

pip install -U mlx-lm

Then:

mlx_lm.generate --model tiiuae/Falcon-E-1B-Instruct --prompt "Implement bubble sort" --max-tokens 100 --temp 0.1

Fine-tuning

For fine-tuning the model, you should load the prequantized revision of the model and use the onebitllms Python package:

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
+ from onebitllms import replace_linear_with_bitnet_linear, quantize_to_1bit

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

tokenizer = AutoTokenizer.from_pretrained(model_id, revision="prequantized")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
+    revision="prequantized"
)
+ model = replace_linear_with_bitnet_linear(model)

trainer = SFTTrainer(
    model,
    ...
)

trainer.train()

+ quantize_to_1bit(output_directory)

Evaluation

We report in the following table our internal pipeline benchmarks:

Note evaluation results are normalized score from former Hugging Face leaderboard v2 tasks

<details> <summary class="bold"> For 1B scale models and below </summary> </details> <details> <summary class="bold"> For 3B scale models </summary> </details>

Below are the results for instruction fine-tuned models:

<details> <summary class="bold"> For 1B scale models and below </summary> </details> <details> <summary class="bold"> For 3B scale models </summary> </details>

Useful links

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

Citation

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

@misc{tiionebitllms,
    title = {Falcon-E, a series of powerful, universal and fine-tunable 1.58bit language models.},
    author = {Falcon-LLM Team},
    month = {April},
    url = {https://falcon-lm.github.io/blog/falcon-edge},
    year = {2025}
}