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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
ArcticTraining is a framework designed to simplify and accelerate the post-training process for large language models (LLMs)
| Date | Stars |
|---|---|
| 2026-07-31 | 287 |
| 2026-08-05 | 288 |
| 2026-08-06 | 288 |
Today
— stars today
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Momentum
0.0
growth rate 0.00%/day
[](https://github.com/snowflakedb/ArcticTraining/blob/main/LICENSE)
[](https://pypi.org/project/arctic-training/)
<h3 align="center">
<img src="docs/images/arctic_training_logo.svg" width=500px><br>
| <a href="https://arctictraining.readthedocs.io/en/latest/"><b>Documentation</b></a> | <a href="https://www.snowflake.com/en/engineering-blog/arctictraining-llm-post-training-framework/"><b>Blog</b></a> |
</h3>
<!--| <a href="#"><b>Discourse</b></a> | -->
## Latest News
* [2026/03] [Post-training Qwen3-32B model on 1x NVIDIA DGX Station with 136K sequence length is now a reality](https://github.com/snowflakedb/ArcticTraining/blob/main/news/qwen3-32b-dgx-station.md)
* [2025/06] [Arctic Long Sequence Training (ALST): Scalable And Efficient Training For Multi-Million Token Sequences](https://www.snowflake.com/en/engineering-blog/arctic-long-sequence-training-multi-million-token-ai/)
* [2025/05] [Fastest Speculative Decoding in vLLM with Arctic Inference and Arctic Training](https://www.snowflake.com/en/engineering-blog/fast-speculative-decoding-vllm-arctic/)
* [2025/03] [Snowflake Arctic Embed Joins ArcticTraining: Simple And Scalable Embedding Model Training](https://www.snowflake.com/en/engineering-blog/arctic-embed-joins-arctictraining/)
* [2025/01] [ArcticTraining: Simplifying and Accelerating Post-Training for LLMs](https://www.snowflake.com/en/engineering-blog/arctictraining-llm-post-training-framework/)
* [2024/12] [SwiftKV: Accelerating Enterprise LLM Workloads with Knowledge Preserving Compute Reduction](https://www.snowflake.com/en/engineering-blog/swiftkv-llm-compute-reduction/)
# ArcticTraining: Simplifying and Accelerating Post-Training for LLMs
ArcticTraining is a framework designed to simplify and accelerate the post-training process for large language models (LLMs). It addresses challenges in current frameworks, such as limited support for rapid prototyping and the lack of native data generation tools, by offering modular trainer designs, simplified code structures, and integrated pipelines for creating and cleaning synthetic data. These features enable users to enhance LLM capabilities, like code generation and complex reasoning, with greater efficiency and flexibility. Read more about ArcticTraining [in our blog](https://www.snowflake.com/en/engineering-blog/arctictraining-llm-post-training-framework/).
# Projects
The projects folder contains various special projects we have released that build on-top of ArcticTraining. Each project includes it's own README and associated assets to get started:
* [SwiftKV](projects/swiftkv)
* [Speculative Decoding](projects/mlp_speculator)
* [Arctic-Embed](projects/arctic_embed)
* [Arctic Long Sequence Training (ALST)](projects/sequence-parallelism)
# Papers
* [Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences](https://arxiv.org/abs/2506.13996)
* [ExCoT: Optimizing Reasoning for Text-to-SQL with Execution Feedback](https://arxiv.org/abs/2503.19988)
* [Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL](https://arxiv.org/abs/2505.20315)
* [SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation](https://arxiv.org/abs/2410.03960)
# Quickstart
To get started training a model with ArcticTraining, follow the steps below:
1. Install the ArcticTraining package and its dependencies:
```bash
pip install arctic-training
```
2. Create a training recipe YAML that uses the built-in Supervised Fine-Tuning (SFT) trainer:
```yaml
type: sft
micro_batch_size: 2
model:
name_or_path: meta-llama/Llama-3.1-8B-Instruct
data:
sources:
- HuggingFaceH4/ultrachat_200k
checkpoint:
- type: huggingface
save_end_of_training: true
output_dir: ./fine-tuned-model
```
3. Run the training recipe with the ArcticTraining CLI (see below). This will use the Excerpt of 5,985 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4ea826f03f8e6447, llm:Repository description: 'ArcticTraining is a framework designed to simplify and accelerate the post-training process for large language models (LLMs)'. Language: Python.
matched fp:4ea826f03f8e6447, llm:Repository description: 'ArcticTraining is a framework designed to simplify and accelerate the post-training process for large language models (LLMs)'. Language: Python.
matched fp:4ea826f03f8e6447, llm:Repository description: 'ArcticTraining is a framework designed to simplify and accelerate the post-training process for large language models (LLMs)'. Language: Python.