v0.2.1

0.2.1 - 2026-07-24

Release notes

• Native Hugging Face chat-template support for text instruction models and static-image VLMs.
• Structured messages, conversations, and conversation batches now work directly with generation, capture, learners, and sweeps.
• Template-specific inputs, rendering choices, and template hashes are recorded in capture fingerprints and artifact provenance.
• Safer instruction prompting through assistant-prefix defaults, duplicate special-token protection, and fail-closed validation for unsupported inputs.

Added

• Hugging Face chat-template generation for structured message mappings,
  conversations, and conversation batches. model.generate(messages=...) is
  supported alongside positional and prompt= inputs.
• chat_template_kwargs on generation, CaptureRequest, and contrastive
  learners for template-specific inputs such as tools, documents, or an
  explicitly selected template.
• Processor-preferred chat rendering for image generation, with tokenizer
  fallback, so instruction-style VLM prompts can use their native template.

Changed

• Chat generation adds an assistant generation prompt by default; capture and
  learner capture retain their explicit default of False.
• Rendered chat text is tokenized with add_special_tokens=False by default
  to avoid duplicating template-owned BOS/EOS/control tokens. An explicit
  tokenizer or processor option may still override that default.
• Capture cache keys include template kwargs, while learner artifact provenance
  records the resolved chat-rendering choice. Exact artifact compatibility now
  checks learned tokenizer-template hashes; sweep evaluation recognizes a
  one-message chat mapping as a prompt rather than generation keyword args.

Fixed

• Single message mappings and raw strings explicitly requesting a template are
  normalized before calling apply_chat_template; mixed raw/chat batches and
  disabled templates for structured messages fail with actionable errors.