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---
library_name: transformers
tags:
- bitnet
- falcon3
---
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Training Details](#training-details)
3. [Usage](#usage)
4. [Evaluation](#evaluation)
5. [Citation](#citation)
# TL;DR
# Model Details
## Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
- **Model type:** Causal decoder-only - instruct / chat version
- **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](https://huggingface.co/blog/1_58_llm_extreme_quantization) and [1-bit LLM paper](https://github.com/microsoft/unilm/blob/master/bitnet/The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ.pdf).
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](https://github.com/microsoft/BitNet) library. You can also play with the model using the [falcon-1.58bit playground](https://huggingface.co/spaces/tiiuae/falcon3-1.58bit-playground) (only for the 7B instruct version).
## 🤗 transformers
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "tiiuae/Falcon3-7B-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/Falcon3-7B-Base-1.58bit -q i2_s
python run_inference.py -m models/Falcon3-7B-Base-1.58bit/ggml-model-i2_s.gguf -p "Hi how are you doing today?" -cnv
```
# Evaluation
Coming soon ..
# Citation
Coming soon .. |