From checkpoint to deployable inputs · 7 min
Package the model artifact
Serving needs weights plus configuration, tokenizer files, and enough metadata to reconstruct the model correctly.
Plain language, the core causal flow, and required checks.
Package what serving needs
A deployable model normally contains weight shards, architecture configuration, tokenizer assets, numeric-format information, and an immutable manifest tying the pieces to one version. Integrity and compatibility checks prevent a partial or mixed release from being marked ready.
The artifact is the handoff between the model factory and the inference system.
Open, closed, and what is actually released
- Closed service: the provider exposes an API; the weights remain private.
- Open weights: the parameter files can be downloaded, but the original data and complete training recipe may remain private.
- Open-ish: weights are available under use, scale, or redistribution restrictions.
- Reproducibly open: weights, code, recipe, and usable data provenance are available under compatible rights.
Interactive explanation
Inspect a model release spectrum
Model release contents
Inspect what a model release actually gives you
Open versus closed is a spectrum of artifacts, rights, operational control, and withheld know-how.
| Release type | Weights | Architecture | Training data | Training code | Recipe |
|---|---|---|---|---|---|
| Closed API | Not provided | Not provided | Not provided | Not provided | Not provided |
| Open weights | Included | Included | Not provided | Not provided | Not provided |
| Open-ish | Included | Included | Not provided | Included | Not provided |
| Reproducible open | Included | Included | Included | Included | Included |
Endpoint access; provider owns every layer.
Owning weights buys control, permanence, and modification. It also makes quantization, batching, KV-cache management, scaling, evaluation, security, and version lifecycle your responsibility. “Free to download” does not mean free to operate.
- Name the main pieces of a model artifact.
- Explain why artifact compatibility matters.
- Weight shards hold learned tensors.
- Configuration describes the architecture.
- Tokenizer assets map text and token IDs.
- Runtime metadata records formats and compatibility.
Core check
Which item is required alongside weights to map text to token IDs?
Terms in this chapter
- Checkpoint
- A saved snapshot of model tensors and associated training or configuration state.
- Tokenizer
- The rules and vocabulary that map text to token IDs and token IDs back to text.
- Model artifact
- The versioned collection of weights, configuration, tokenizer assets, and metadata needed to reconstruct a model.
Primary references
- Transformers model loading ↗Hugging Face
Primary runtime documentation for model configuration and state dictionaries.
- Tokenizer API ↗Hugging Face
Primary reference for producing model input IDs and attention metadata.