
ArcticTraining
ArcticTraining is a framework designed to simplify and accelerate the post-training process for large language models (LLMs)
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ArcticTraining is a framework designed to simplify and accelerate the post-training process for large language models (LLMs). It offers modular trainer designs, simplified code structures, and integrated pipelines for creating and cleaning synthetic data, enabling users to enhance LLM capabilities like code generation and complex reasoning with greater efficiency and flexibility.
README:

| Documentation | Blog |
- [2025/03] Snowflake Arctic Embed Joins ArcticTraining: Simple And Scalable Embedding Model Training
- [2025/01] ArcticTraining: Simplifying and Accelerating Post-Training for LLMs
- [2024/12] SwiftKV: Accelerating Enterprise LLM Workloads with Knowledge Preserving Compute Reduction
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.
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:
To get started training a model with ArcticTraining, follow the steps below:
- Install the ArcticTraining package and its dependencies:
pip install arctic-training
- Create a training recipe YAML that uses the built-in Supervised Fine-Tuning (SFT) trainer:
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
- Run the training recipe with the ArcticTraining CLI (see below). This will use the
DeepSpeed
launcher behind the scenes, you can pass any compatible DeepSpeed launcher arguments to the ArcticTraining CLI (e.g., --num_nodes, --num_gpus).
arctic_training path/to/sft-recipe.yaml
To customize the training workflow, you can modify the training recipe YAML we created in step 3 above. For example, you can change the model, dataset, checkpoint, or other settings to meet your specific requirements. A full list of configuration options can be found on the configuration documentation page.
If you want to create a new trainer, you can do so by subclassing the
Trainer
or SFTTrainer
classes and implementing the necessary
modifications. For example, you could create a new trainer from SFTTrainer
that uses a different loss function:
from arctic_training import SFTTrainer
class CustomTrainer(SFTTrainer):
name = "my_custom_trainer"
def loss(self, batch):
# Custom loss function implementation
return loss
This new trainer will be automatically registered with ArcticTraining when the
script containing the declaration of CustomTrainer
is imported. By default,
ArcticTraining looks for a train.py
in the current working directory to find
custom trainers. You can also specify a custom path to the trainers with the
code
field in your training recipe:
type: my_custom_trainer
code: path/to/custom_trainers.py
model:
name_or_path: meta-llama/Llama-3.1-8B-Instruct
data:
sources:
- HuggingFaceH4/ultrachat_200k
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