deepseek-ocr.rs 🚀

Rust implementation of the DeepSeek-OCR inference stack with a fast CLI and an OpenAI-compatible HTTP server. The workspace packages multiple OCR backends, prompt tooling, and a serving layer so you can build document understanding pipelines that run locally on CPU, Apple Metal, or (alpha) NVIDIA CUDA GPUs.

中文文档请看 README_CN.md。

Want ready-made binaries? Latest macOS (Metal-enabled) and Windows bundles live in the build-binaries workflow artifacts. Grab them from the newest green run.

Choosing a Model 🔬

*Measured from the default FP16 safetensors. Runtime footprint varies with sequence length.

Guidance:

• Need maximum fidelity, multi-region reasoning, or already have 16–24GB VRAM? Use DeepSeek‑OCR. The hybrid SAM+CLIP tower plus DeepSeek‑V2 MoE decoder handles complex layouts best, but expect higher memory/latency.
• Deploying to CPU-only nodes, 16GB laptops, or latency-sensitive services? Choose PaddleOCR‑VL. Its dense Ernie decoder (18 layers, hidden 1024) activates fewer parameters per token and keeps memory under 10GB while staying close in quality on most docs.
• Chasing reading-order accuracy, layout grounding, or multi-page multilingual PDFs on roomy hardware? Pick DotsOCR with BF16 on Metal/CUDA. Prefill runs around 40–50 tok/s on M-series GPUs but can fall to ~12 tok/s on CPU because of the heavy vision tower.

Why Rust? 💡

The original DeepSeek-OCR ships as a Python + Transformers stack—powerful, but hefty to deploy and awkward to embed. Rewriting the pipeline in Rust gives us:

• Smaller deployable artifacts with zero Python runtime or conda baggage.
• Memory-safe, thread-friendly infrastructure that blends into native Rust backends.
• Unified tooling (CLI + server) running on Candle + Rocket without the Python GIL overhead.
• Drop-in compatibility with OpenAI-style clients while tuned for single-turn OCR prompts.

Technical Stack ⚙️

• Candle for tensor compute, with Metal and CUDA backends and FlashAttention support.
• Rocket + async streaming for OpenAI-compatible /v1/responses and /v1/chat/completions.
• tokenizers (upstream DeepSeek release) wrapped by crates/assets for deterministic caching via Hugging Face and ModelScope mirrors.
• Pure Rust vision/prompt pipeline shared by CLI and server to avoid duplicated logic.

Advantages over the Python Release 🥷

• Faster cold-start on Apple Silicon, lower RSS, and native binary distribution.
• Deterministic dual-source (Hugging Face + ModelScope) asset download + verification built into the workspace.
• Automatic single-turn chat compaction so OCR outputs stay stable even when clients send history.
• Ready-to-use OpenAI compatibility for tools like Open WebUI without adapters.

Highlights ✨

• One repo, two entrypoints – a batteries-included CLI for batch jobs and a Rocket-based server that speaks /v1/responses and /v1/chat/completions.
• Works out of the box – pulls model weights, configs, and tokenizer from whichever of Hugging Face or ModelScope responds fastest on first run.
• Optimised for Apple Silicon – optional Metal backend with FP16 execution for real-time OCR on laptops.
• CUDA (alpha) – experimental support via --features cuda + --device cuda --dtype f16; expect rough edges while we finish kernel coverage.
• Intel MKL (preview) – faster BLAS on x86 via --features mkl (install Intel oneMKL beforehand).
• OpenAI client compatibility – drop-in replacement for popular SDKs; the server automatically collapses chat history to the latest user turn for OCR-friendly prompts.

Model Matrix 📦

The workspace exposes three base model IDs plus DSQ-quantized variants for DeepSeek‑OCR, PaddleOCR‑VL, and DotsOCR:

Quick Start 🏁

Prerequisites

• Rust 1.78+ (edition 2024 support)
• Git
• Optional: Apple Silicon running macOS 13+ for Metal acceleration
• Optional: CUDA 12.2+ toolkit + driver for experimental NVIDIA GPU acceleration on Linux/Windows
• Optional: Intel oneAPI MKL for preview x86 acceleration (see below)
• (Recommended) Hugging Face account with HF_TOKEN when pulling from the deepseek-ai/DeepSeek-OCR repo (ModelScope is used automatically when it’s faster/reachable).

Clone the Workspace

git clone https://github.com/TimmyOVO/deepseek-ocr.rs.git
cd deepseek-ocr.rs
cargo fetch

Model Assets

The first invocation of the CLI or server downloads the config, tokenizer, and model-00001-of-000001.safetensors (~6.3GB) into DeepSeek-OCR/. To prefetch manually:

cargo run -p deepseek-ocr-cli --release -- --help # dev profile is extremely slow; always prefer --release

Always include --release when running from source; debug builds on this model are extremely slow. Set HF_HOME/HF_TOKEN if you store Hugging Face caches elsewhere (ModelScope downloads land alongside the same asset tree). The full model package is ~6.3GB on disk and typically requires ~13GB of RAM headroom during inference (model + activations).

Configuration & Overrides 🗂️

The CLI and server share the same configuration. On first launch we create a config.toml populated with defaults; later runs reuse it so both entrypoints stay in sync.

• Override the location with --config /path/to/config.toml (available on both CLI and server). Missing files are created automatically.
• Each [models.entries."<id>"] record can point to custom config, tokenizer, or weights files. When omitted we fall back to the cache directory above and download/update assets as required.
• Runtime values resolve in this order: command-line flags → values stored in config.toml → built-in defaults. The HTTP API adds a final layer where request payload fields (for example max_tokens) override everything else for that call.

The generated file starts with the defaults below; adjust them to persistently change behaviour:

[models]
active = "deepseek-ocr"

[models.entries.deepseek-ocr]

[inference]
device = "cpu"
template = "plain"
base_size = 1024
image_size = 640
crop_mode = true
max_new_tokens = 512
use_cache = true

[server]
host = "0.0.0.0"
port = 8000

• [models] picks the active model and lets you add more entries (each entry can point to its own config/tokenizer/weights).
• [inference] controls notebook-friendly defaults shared by the CLI and server (device, template, vision sizing, decoding budget, cache usage).
• [server] sets the network binding and the model identifier reported by /v1/models.

See crates/cli/README.md and crates/server/README.md for concise override tables.

Benchmark Snapshot 📊

Single-request Rust CLI (Accelerate backend on macOS) compared with the reference Python pipeline on the same prompt and image:

Command-Line Interface 🖥️

Build and run directly from the workspace:

cargo run -p deepseek-ocr-cli --release -- \
  --prompt "<image>\n<|grounding|>Convert this receipt to markdown." \
  --image baselines/sample/images/test.png \
  --device cpu --max-new-tokens 512

Tip: --release is required for reasonable throughput; debug builds can be 10x slower.

macOS tip: append --features metal to the cargo run/cargo build commands to compile with Accelerate + Metal backends.

CUDA tip (Linux/Windows): append --features cuda and run with --device cuda --dtype f16 to target NVIDIA GPUs—feature is still alpha, so be ready for quirks.

Intel MKL preview: install Intel oneMKL, then build with --features mkl for faster CPU matmuls on x86.

Install the CLI as a binary:

cargo install --path crates/cli
deepseek-ocr-cli --help

Key flags:

• --prompt / --prompt-file: text with <image> slots
• --image: path(s) matching <image> placeholders
• --device and --dtype: choose metal + f16 on Apple Silicon or cuda + f16 on NVIDIA GPUs
• --max-new-tokens: decoding budget
• Sampling controls: --do-sample, --temperature, --top-p, --top-k, --repetition-penalty, --no-repeat-ngram-size, --seed
• By default decoding stays deterministic (do_sample=false, temperature=0.0, no_repeat_ngram_size=20)
• To use stochastic sampling set --do-sample true --temperature 0.8 (and optionally adjust the other knobs)

Switching Models

The autogenerated config.toml now lists three entries:

• deepseek-ocr (default) – the original DeepSeek vision-language stack.
• paddleocr-vl – the PaddleOCR-VL 0.9B SigLIP + Ernie release.
• dots-ocr – the Candle port of dots.ocr with DotsVision + Qwen2 (use BF16 on Metal/CUDA if possible; see the release matrix for memory notes).

Pick which one to load via --model:

deepseek-ocr-cli --model paddleocr-vl --prompt "<image> Summarise"

The CLI (and server) will download the matching config/tokenizer/weights from the appropriate repository (deepseek-ai/DeepSeek-OCR, PaddlePaddle/PaddleOCR-VL, or dots-ocr) into your cache on first use. You can still override paths with --model-config, --tokenizer, or --weights if you maintain local fine-tunes.

HTTP Server ☁️

Launch an OpenAI-compatible endpoint:

cargo run -p deepseek-ocr-server --release -- \
  --host 0.0.0.0 --port 8000 \
  --device cpu --max-new-tokens 512

Keep --release on the server as well; the debug profile is far too slow for inference workloads. macOS tip: add --features metal to the cargo run -p deepseek-ocr-server command when you want the server binary to link against Accelerate + Metal (and pair it with --device metal at runtime).

CUDA ti