# Falcon-Edge: A series of powerful, universal, fine-tunable 1.58bit language models.

DevFeed: [Falcon-Edge: A series of powerful, universal, fine-tunable 1.58bit language models.](<https://devfeed.tech/articles/falcon-edge-a-series-of-powerful-universal-fine-tunable-1-58bit-language-models-7507.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/tiiuae/falcon-edge>)

Author: Younes B; Qiyang Zhao; Hang Zou; Rhaiem; Ilyas Chahed; Maksim Velikanov; Jingwei Zuo; Mike Lubinets; Hakim Hacid; Falcon LLM TII UAE

Published: 2025-05-15T13:13:45Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [LLM Techniques](<https://devfeed.tech/topics/llm-techniques.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [Compression](<https://devfeed.tech/topics/compression.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [AI Inference](<https://devfeed.tech/topics/ai-inference.md>)

Tags: [compression](<https://devfeed.tech/tags/compression.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [floating-point](<https://devfeed.tech/tags/floating-point.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [precision](<https://devfeed.tech/tags/precision.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [train](<https://devfeed.tech/tags/train.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

The article introduces Falcon-Edge, a series of 1.58-bit language models based on the BitNet architecture. The models use ternary weights during training to reduce memory use and improve deployment efficiency, and are offered in 1-billion- and 3-billion-parameter base and instruction-tuned variants. The article describes their training approach, fine-tuning variants, and evaluation on the former Hugging Face leaderboard v2 benchmark.

## Source excerpt

A Blog post by Technology Innovation Institute on Hugging Face