Instructions to use sabafallah/Unlimited-OCR-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sabafallah/Unlimited-OCR-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sabafallah/Unlimited-OCR-GGUF",
	filename="mmproj-unlimited-ocr-bf16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sabafallah/Unlimited-OCR-GGUF 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 sabafallah/Unlimited-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sabafallah/Unlimited-OCR-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sabafallah/Unlimited-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sabafallah/Unlimited-OCR-GGUF: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 sabafallah/Unlimited-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sabafallah/Unlimited-OCR-GGUF: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 sabafallah/Unlimited-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sabafallah/Unlimited-OCR-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sabafallah/Unlimited-OCR-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use sabafallah/Unlimited-OCR-GGUF with Ollama:
```
ollama run hf.co/sabafallah/Unlimited-OCR-GGUF:Q4_K_M
```

Unsloth Studio

How to use sabafallah/Unlimited-OCR-GGUF 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 sabafallah/Unlimited-OCR-GGUF 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 sabafallah/Unlimited-OCR-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sabafallah/Unlimited-OCR-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use sabafallah/Unlimited-OCR-GGUF with Docker Model Runner:
```
docker model run hf.co/sabafallah/Unlimited-OCR-GGUF:Q4_K_M
```

Lemonade

How to use sabafallah/Unlimited-OCR-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sabafallah/Unlimited-OCR-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Unlimited-OCR-GGUF-Q4_K_M

List all available models

lemonade list

Unlimited-OCR GGUF (for llama.cpp)

GGUF model files for running baidu/Unlimited-OCR in llama.cpp's MTMD (multimodal) support.

Unlimited-OCR is DeepSeek-OCR v1's DeepEncoder + a DeepSeek-V2 MoE decoder, with the decoder attention replaced by R-SWA (Reference Sliding Window Attention). llama.cpp support lands in two stacked PRs (the same GGUF files work for both):

PR1 - converter, decoder runs full MHA: #24969
PR2 - R-SWA in the decoder (stacked on PR1): #24975

These models are only compatible with the PR branches, not upstream llama.cpp master.

A third PR is independent of the stack and targets master directly:

max_tiles fix: #25614

The mmproj files were re-uploaded on 2026-07-13 and now carry preproc_max_tiles = 32, matching the reference implementation. Unlimited-OCR shares DeepSeek-OCR v1's projector type, so without #25614 llama.cpp ignores the key and falls back to v1's cap of 9 - tall or dense pages are then split into a coarser tile grid than the reference. Older mmproj files keep working either way.

Download

huggingface-cli download sabafallah/Unlimited-OCR-GGUF --include "unlimited-ocr-bf16.gguf" --local-dir gguf_models/baidu
huggingface-cli download sabafallah/Unlimited-OCR-GGUF --include "mmproj-unlimited-ocr-bf16.gguf" --local-dir gguf_models/baidu

Run example

llama-mtmd-cli:

build/bin/llama-mtmd-cli \
  -m gguf_models/baidu/unlimited-ocr-bf16.gguf \
  --mmproj gguf_models/baidu/mmproj-unlimited-ocr-bf16.gguf \
  --image tools/mtmd/test-1.jpeg -p "document parsing." \
  --chat-template deepseek-ocr --no-jinja \
  --temp 0 --flash-attn off --no-warmup \
  -n 4096 -c 16384 \
  --dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 35 \
  --dry-penalty-last-n 128 --dry-sequence-breaker none

The DRY flags are a weak loop guard mirroring the reference implementation's no_repeat_ngram_size=35 / ngram_window=128. Keep the allowed length high: aggressive DRY settings garble the model's HTML table output.

Files

file
unlimited-ocr-bf16.gguf	LM bf16
unlimited-ocr-q8_0.gguf	LM q8_0
unlimited-ocr-Q4_K_M.gguf	LM Q4_K_M
mmproj-unlimited-ocr-bf16.gguf	mmproj bf16
mmproj-unlimited-ocr-q8_0.gguf	mmproj q8_0

Downloads last month: 7,006

GGUF

Model size

3B params

Architecture

deepseek2-ocr

Hardware compatibility

4-bit

8-bit

16-bit

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