Principle:Allenai Open instruct Model Publishing
| Knowledge Sources | |
|---|---|
| Domains | Machine Learning, MLOps, Open Science |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Model publishing is the process of uploading trained model artifacts (weights, tokenizer, configuration) to a model hub so they can be shared, versioned, and accessed by other researchers or systems.
Description
After training a language model, the resulting checkpoint must be made accessible for evaluation, deployment, or further training. Model publishing to a hub (such as the HuggingFace Hub) serves several purposes:
Sharing and collaboration: Published models can be accessed by other team members, evaluation pipelines, or the broader research community. The Hub provides a standardized interface for model discovery and download.
Versioning: Each upload creates a commit in the model repository, providing a complete history of model versions. Specific revisions can be loaded by referencing a branch name, tag, or commit hash.
Reproducibility: By publishing models alongside their training configuration and dataset metadata, others can verify or build upon the results. The Hub stores all artifacts needed to load and use the model.
Automated evaluation: Many evaluation frameworks (such as Beaker-based eval in Open Instruct) expect models to be available on the Hub. Publishing enables automated downstream evaluation pipelines to be triggered immediately after training.
Access control: Models can be published as private (accessible only to the owner or organization) or public. Sensitive or in-progress models are typically published privately, with public release after quality validation.
Usage
Publish models after training completes successfully and quality checks pass. For iterative development, publish to a private repository with branch-based versioning. For final releases, publish to a public repository with a descriptive model card.
Theoretical Basis
Model artifact structure: A published model repository contains:
model_repo/
config.json # Model architecture configuration
pytorch_model.bin # Model weights (or sharded files)
tokenizer.json # Tokenizer vocabulary
tokenizer_config.json # Tokenizer settings and chat template
special_tokens_map.json # Special token definitions
generation_config.json # Default generation parameters
README.md # Model card (optional)
Versioning model:
repo_id = "{entity}/{model_name}"
revision = "{branch_or_tag_or_commit}"
# Each training run creates a branch:
revision = exp_name (e.g., "sft_tulu3_8b_2024-01-15_seed42")
# Loading a specific version:
model = AutoModelForCausalLM.from_pretrained(repo_id, revision=revision)
Upload atomicity: The Hub API's upload_folder() uploads all files in a single commit, ensuring the published state is always consistent (no partial uploads).