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v0.4.0

nestjs/nestv0.4.0Nov 9, 2025by github-actions[bot]

AI Summary

This release adds the PaddleOCR-VL backend, introduces lazy loading and sampling controls, and refactors common utilities to reduce code duplication.

Key Highlights

  • New PaddleOCR-VL backend (SigLIP + Ernie 0.9B) selectable alongside DeepSeek-OCR.
  • Lazy loading implementation to defer weight mmap and reduce startup time.
  • Extended server and CLI sampling controls for temperature, top-p, top-k, etc.
  • Refactoring of common utilities into deepseek-ocr-core crate.

New Features

  • PaddleOCR-VL backend support.
  • Lazy weight loading feature.
  • Sampling controls (--do-sample, --temperature, --top-p, --top-k, --repetition-penalty).
  • Model-aware bilingual Markdown prompts.

Full Release Notes

## New Features

  - `PaddleOCR‑VL` backend (SigLIP + Ernie 0.9B with FlashAttention) is now selectable alongside `DeepSeek‑OCR`. Documentation covers model switching and architecture/memory differences.
  - Model-aware prompts with bilingual Markdown feedback when requests omit <image> placeholders.
  - Lazy loading: the server defers weight mmap until the first request, reducing startup time.
  - Server/CLI sampling controls: `--do-sample`, `--temperature`, `--top-p`, `--top-k`, `--repetition-penalty`, and `--no-repeat-ngram-size` are now recognized across both entry points.

  ## Improvements

  - Common utilities (token sampling, embedding gathers, transformer KV cache) moved into deepseek-ocr-core, trimming duplication between DeepSeek and Paddle crates.
  - Documentation clarifies multi-model selection, DeepSeek-only dynamic crop mode, bilingual terminology, and per-flag behavior.
  - CLI/Server prompt builders choose the correct format per model, improving output quality without manual tweaks.

  ## Bug Fixes

  - ModelScope provider now respects arbitrary repo IDs and exact file paths, fixing Paddle asset downloads that previously fetched the wrong config.json.
  - Requests without images no longer throw transport errors; both sync and streaming responses return a structured bilingual warning instead.
  - Prompt/image mismatches surface as normal assistant replies instead of opaque “prompt formatting failed” errors, keeping clients compatible with standard OpenAI flows.