Instructions to use batiai/batisee with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use batiai/batisee with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="batiai/batisee",
	filename="batisee-text-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use batiai/batisee with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf batiai/batisee:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf batiai/batisee:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf batiai/batisee:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf batiai/batisee:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf batiai/batisee:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf batiai/batisee:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf batiai/batisee:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf batiai/batisee:Q4_K_M

Use Docker

docker model run hf.co/batiai/batisee:Q4_K_M

LM Studio
Jan

vLLM

How to use batiai/batisee with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "batiai/batisee"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "batiai/batisee",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/batiai/batisee:Q4_K_M

Ollama
How to use batiai/batisee with Ollama:
```
ollama run hf.co/batiai/batisee:Q4_K_M
```

Unsloth Studio

How to use batiai/batisee with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for batiai/batisee to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for batiai/batisee to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for batiai/batisee to start chatting

Atomic Chat new
Docker Model Runner
How to use batiai/batisee with Docker Model Runner:
```
docker model run hf.co/batiai/batisee:Q4_K_M
```

Lemonade

How to use batiai/batisee with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull batiai/batisee:Q4_K_M

Run and chat with the model

lemonade run user.batisee-Q4_K_M

List all available models

lemonade list

batisee — On-device Korean Document OCR by BatiAI

ℹ️ Ollama: batisee uses the brand-new DeepSeek-OCR (deepseek2ocr) architecture, which the bundled Ollama engine does not load yet. Run it today with llama.cpp (below); Ollama support will follow once the engine merges this architecture.

batisee is BatiAI's on-device document-OCR model — part of the BatiAI perception family (batisay = speech-to-text, batispeak = diarization, batisee = document/OCR).

Built on baidu/Unlimited-OCR (DeepSeek-OCR architecture, MIT), converted to GGUF directly from the original weights by BatiAI (not a re-host of community quants), BatiAI-signed, and verified for Korean so you can run it on a Mac with confidence.

batisee 는 BatiAI 인지(perception) 제품군의 문서 OCR 모델입니다 (batisay=음성인식, batispeak=화자분리, batisee=문서/OCR). baidu/Unlimited-OCR(DeepSeek-OCR 아키텍처, MIT)를 베이스로, 원본 가중치에서 BatiAI가 직접 GGUF 변환(타사 양자화물 재배포 아님)하고, BatiAI 서명 + 한국어 검증을 거쳐 Mac에서 바로 쓰도록 패키징했습니다.

Why batisee?

On-device — runs locally on a Mac (no cloud, no upload). Q4_K_M is 1.9 GB.
Korean-verified — measured on rendered Korean documents (see results below): clean text CER 0%, hard document (small font + table + blur) 100% key-content recall with table structure preserved.
Document-native — outputs layout boxes (<|det|>) and converts tables to HTML <table>.
Our own conversion — GGUF built directly from baidu/Unlimited-OCR original safetensors, BatiAI-signed (general.author = BatiAI).
MIT — fully commercial-friendly.

⭐ Korean OCR results / 한국어 OCR 검증

Rendered Korean documents (ground-truth known) → OCR → compared. Method & images: ocr-poc/gate-results.

Test / 테스트	Difficulty / 난이도	Hangul kept / 한글보존	Key recall / 핵심recall	Table / 표	CER
Gate 1 (clean)	clean text	100%	—	—	0.0%
Gate 2 (hard)	small font + table + blur	100%	100%	✅ `<table>`	—

Both Q8_0 and Q4_K_M pass with no degradation and no decoding loops. Q8/Q4 모두 품질 저하·디코딩 루프 없이 통과.

Available files

File	Size	Use
`batisee-text-Q8_0.gguf`	3.0 GB	highest quality / 최고품질
`batisee-text-Q4_K_M.gguf`	1.9 GB	16 GB Mac sweet spot (recommended)
`mmproj-batisee-BF16.gguf`	826 MB	vision encoder (required) / 비전 인코더(필수)

How to run (llama.cpp)

⚠️ This is a multimodal model — you always need both the text GGUF and mmproj-batisee-BF16.gguf.

🍎 On a Mac: brew install llama.cpp (version ≥ 9430) provides llama-mtmd-cli and loads batisee directly — verified on M4 Max, no source build needed.

hf download batiai/batisee --include "batisee-text-Q4_K_M.gguf" --include "mmproj-batisee-BF16.gguf" --local-dir ./batisee

llama-mtmd-cli \
    -m ./batisee/batisee-text-Q4_K_M.gguf \
    --mmproj ./batisee/mmproj-batisee-BF16.gguf \
    --image your-document.png \
    -p "document parsing." \
    --jinja --temp 0 --repeat-penalty 1.05 -ngl 99

⭐ Recipe matters (learned the hard way)

Flag	Why
`-p "document parsing."`	The prompt must be this. `"Free OCR."` triggers a buggy reasoning mode that emits meta-commentary instead of the text.
`--jinja`	Without it the chat-template step crashes.
`--temp 0 --repeat-penalty 1.05`	Without the penalty the decoder can fall into an infinite repeat loop.

Model details

Base: baidu/Unlimited-OCR — DeepSeek-OCR architecture
- Text: DeepSeek-3B-MoE (12 layers, 64 routed experts top-6, standard MHA, 32K context) → deepseek2ocr
- Vision: DeepEncoder (CLIP-L-14 + SAM-ViT-B, 1024px) + linear projector
Conversion: built directly from original safetensors with llama.cpp (DeepSeek-OCR support). Image normalization mean = std = [0.5, 0.5, 0.5].
License: MIT (inherited)

BatiAI signing

All GGUFs carry:

general.author = BatiAI
general.url = https://flow.bati.ai

Attribution & License

This model is a GGUF distribution of baidu/Unlimited-OCR (MIT), which is built on the DeepSeek-OCR architecture. Original authors' work and license are retained; BatiAI's contribution is the from-original GGUF conversion, signing, Korean verification, and on-device packaging.

본 모델은 baidu/Unlimited-OCR(MIT)의 GGUF 배포본입니다. 원저작자 작업·라이선스를 유지하며, BatiAI 기여는 원본에서의 직접 GGUF 변환·서명·한국어 검증·온디바이스 패키징입니다.

Roadmap

Korean-specialized fine-tuning (handwriting / camera photos / low-quality scans) — current verification covers clean digital-rendered text; real-world robustness is the next milestone.
한국어 특화 파인튜닝(손글씨·카메라 사진·저품질 스캔) 예정 — 현 검증은 디지털 렌더 텍스트 기준.

About BatiFlow

BatiFlow — free, unlimited, on-device AI for Mac.

On-device benchmark — MacBook Pro M4 Max (Q4_K_M)

Measured with brew llama-mtmd-cli 9430, on the same 4 stress documents as the desktop GPU.

Metric	Value
Engine	Homebrew `llama.cpp` (`llama-mtmd-cli`) 9430 — loads `deepseek2ocr` fine, no source build needed
Page latency (full pipeline)	~3.0 s/page cold, ~3 s warm (≈ desktop GPU's 2.56 s/page)
Memory (max RSS)	2.94 GB (peak 2.97 GB)
Quality	digital docs/tables near-perfect (numbers 100%, occasional single KR-glyph slip); heavy degradation / skew = known limits → v2 roadmap

tokens/sec and standalone mmproj-encode time are not emitted by the 9430 Homebrew bottle (its perf block is suppressed); available via a source build if needed. Page latency + RSS are the user-facing numbers and confirm M4 Max ≈ desktop-GPU class.

Downloads last month: 102

GGUF

Model size

3B params

Architecture

deepseek2-ocr

Hardware compatibility

4-bit

8-bit

Model tree for batiai/batisee

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