Table of Contents
TL;DR
Model Details
This is a 'hacked' version of tiiuae/Falcon3-10B-Base-1.58bit where model weight scales have been injected into ternary model weights in order to make the model compatible with fine-tuning
Model Description
- Developed by: https://www.tii.ae
- Model type: Causal decoder-only
- Architecture: Pure-transformer - 1.58bit version
- Language(s) (NLP): Mainly English
- License: TII Falcon License 2.0
Training details
The model has been trained following the training strategies from the recent 1-bit LLM HF blogpost and 1-bit LLM paper. For more details about the training protocol of this model, please refer to the Falcon-3 technical report, section Compression.
Usage
Currently to use this model you can either rely on Hugging Face transformers library or BitNet library. You can also play with the model using the falcon-1.58bit playground (only for the 7B instruct version).
🤗 transformers
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "tiiuae/Falcon3-10B-Base-1.58bit"
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
).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/Falcon3-10B-Base-1.58bit -q i2_s
python run_inference.py -m models/Falcon3-10B-1.58bit/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
Evaluation
We report in the following table our internal pipeline benchmarks:
Note evaluation results are normalized score from v2 leaderboard tasks - reported results of original models in the blogpost are raw scores
Citation
Coming soon ..