mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-07 06:40:04 +06:00
6c08b3b6e5
* Add Falcon3 documentation * Update Falcon3 documentation * Change Falcon to Falcon3 * Update docs and run make fix-copies * Add blog post and huggingface models links
30 lines
2.2 KiB
Markdown
30 lines
2.2 KiB
Markdown
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
|
the License. You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
|
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
|
specific language governing permissions and limitations under the License.
|
|
|
|
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
|
|
rendered properly in your Markdown viewer.
|
|
|
|
-->
|
|
|
|
# Falcon3
|
|
|
|
## Overview
|
|
|
|
Falcon3 represents a natural evolution from previous releases, emphasizing expanding the models' science, math, and code capabilities. This iteration includes five base models: Falcon3-1B-Base, Falcon3-3B-Base, Falcon3-Mamba-7B-Base, Falcon3-7B-Base, and Falcon3-10B-Base. In developing these models, we incorporated several key innovations aimed at improving the models' performances while reducing training costs:
|
|
|
|
One pre-training: We conducted a single large-scale pretraining run on the 7B model, using 2048 H100 GPU chips, leveraging 14 trillion tokens featuring web, code, STEM, and curated high-quality and multilingual data.
|
|
Depth up-scaling for improved reasoning: Building on recent studies on the effects of model depth, we upscaled the 7B model to a 10B parameters model by duplicating the redundant layers and continuing pre-training with 2TT of high-quality data. This yielded Falcon3-10B-Base which achieves state-of-the-art zero-shot and few-shot performance for models under 13B parameters.
|
|
Knowledge distillation for better tiny models: To provide compact and efficient alternatives, we developed Falcon3-1B-Base and Falcon3-3B-Base by leveraging pruning and knowledge distillation techniques, using less than 100GT of curated high-quality data, thereby redefining pre-training efficiency.
|
|
|
|
## Resources
|
|
- [Blog post](https://huggingface.co/blog/falcon3)
|
|
- [Models on Huggingface](https://huggingface.co/collections/tiiuae/falcon3-67605ae03578be86e4e87026)
|