Instructions to use prithivMLmods/Nanonets-OCR-s-AIO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Nanonets-OCR-s-AIO-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/Nanonets-OCR-s-AIO-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Nanonets-OCR-s-AIO-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Nanonets-OCR-s-AIO-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Nanonets-OCR-s-AIO-GGUF",
	filename="Nanonets-OCR-s.IQ4_XS.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prithivMLmods/Nanonets-OCR-s-AIO-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Nanonets-OCR-s-AIO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Nanonets-OCR-s-AIO-GGUF:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/prithivMLmods/Nanonets-OCR-s-AIO-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Nanonets-OCR-s-AIO-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Nanonets-OCR-s-AIO-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Nanonets-OCR-s-AIO-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/prithivMLmods/Nanonets-OCR-s-AIO-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Nanonets-OCR-s-AIO-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Nanonets-OCR-s-AIO-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Nanonets-OCR-s-AIO-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Nanonets-OCR-s-AIO-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Nanonets-OCR-s-AIO-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use prithivMLmods/Nanonets-OCR-s-AIO-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Nanonets-OCR-s-AIO-GGUF:Q4_K_M
```

Unsloth Studio new

How to use prithivMLmods/Nanonets-OCR-s-AIO-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 prithivMLmods/Nanonets-OCR-s-AIO-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 prithivMLmods/Nanonets-OCR-s-AIO-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use prithivMLmods/Nanonets-OCR-s-AIO-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Nanonets-OCR-s-AIO-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Nanonets-OCR-s-AIO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Nanonets-OCR-s-AIO-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nanonets-OCR-s-AIO-GGUF-Q4_K_M

List all available models

lemonade list

Nanonets-OCR-s-AIO-GGUF

Nanonets-OCR-s is a state-of-the-art image-to-markdown OCR model that transforms complex documents into structured markdown with intelligent content recognition and semantic tagging. It goes beyond simple text extraction by accurately recognizing LaTeX equations, images, signatures, watermarks, checkboxes, and complex tables, making the output highly suitable for downstream processing by Large Language Models (LLMs). Built on the Qwen2.5-VL-3B Visual-Language Model and trained on over 250,000 diverse pages—including research papers, legal and financial documents, healthcare forms, and more—the model excels in preserving document structure and semantic context. It supports intelligent tagging of visual elements and special components, enabling efficient document understanding and automation in academic, legal, healthcare, and corporate workflows.

Model Files

File Name	Quant Type	File Size
Nanonets-OCR-s.f16.gguf	F16	6.18 GB
Nanonets-OCR-s.Q2_K.gguf	Q2_K	1.27 GB
Nanonets-OCR-s.Q3_K_L.gguf	Q3_K_L	1.71 GB
Nanonets-OCR-s.Q3_K_M.gguf	Q3_K_M	1.59 GB
Nanonets-OCR-s.Q3_K_S.gguf	Q3_K_S	1.45 GB
Nanonets-OCR-s.Q4_K_M.gguf	Q4_K_M	1.93 GB
Nanonets-OCR-s.Q4_K_S.gguf	Q4_K_S	1.83 GB
Nanonets-OCR-s.Q5_K_M.gguf	Q5_K_M	2.22 GB
Nanonets-OCR-s.Q5_K_S.gguf	Q5_K_S	2.17 GB
Nanonets-OCR-s.Q6_K.gguf	Q6_K	2.54 GB
Nanonets-OCR-s.Q8_0.gguf	Q8_0	3.29 GB
Nanonets-OCR-s.IQ4_XS.gguf	IQ4_XS	1.75 GB
Nanonets-OCR-s.i1-IQ1_M.gguf	i1-IQ1_M	850 MB
Nanonets-OCR-s.i1-IQ1_S.gguf	i1-IQ1_S	791 MB
Nanonets-OCR-s.i1-IQ2_M.gguf	i1-IQ2_M	1.14 GB
Nanonets-OCR-s.i1-IQ2_S.gguf	i1-IQ2_S	1.06 GB
Nanonets-OCR-s.i1-IQ2_XS.gguf	i1-IQ2_XS	1.03 GB
Nanonets-OCR-s.i1-IQ2_XXS.gguf	i1-IQ2_XXS	948 MB
Nanonets-OCR-s.i1-IQ3_M.gguf	i1-IQ3_M	1.49 GB
Nanonets-OCR-s.i1-IQ3_S.gguf	i1-IQ3_S	1.46 GB
Nanonets-OCR-s.i1-IQ3_XS.gguf	i1-IQ3_XS	1.39 GB
Nanonets-OCR-s.i1-IQ3_XXS.gguf	i1-IQ3_XXS	1.28 GB
Nanonets-OCR-s.i1-IQ4_NL.gguf	i1-IQ4_NL	1.83 GB
Nanonets-OCR-s.i1-IQ4_XS.gguf	i1-IQ4_XS	1.74 GB
Nanonets-OCR-s.i1-Q2_K.gguf	i1-Q2_K	1.27 GB
Nanonets-OCR-s.i1-Q2_K_S.gguf	i1-Q2_K_S	1.2 GB
Nanonets-OCR-s.i1-Q3_K_L.gguf	i1-Q3_K_L	1.71 GB
Nanonets-OCR-s.i1-Q3_K_M.gguf	i1-Q3_K_M	1.59 GB
Nanonets-OCR-s.i1-Q3_K_S.gguf	i1-Q3_K_S	1.45 GB
Nanonets-OCR-s.i1-Q4_0.gguf	i1-Q4_0	1.83 GB
Nanonets-OCR-s.i1-Q4_1.gguf	i1-Q4_1	2 GB
Nanonets-OCR-s.i1-Q4_K_M.gguf	i1-Q4_K_M	1.93 GB
Nanonets-OCR-s.i1-Q4_K_S.gguf	i1-Q4_K_S	1.83 GB
Nanonets-OCR-s.i1-Q5_K_M.gguf	i1-Q5_K_M	2.22 GB
Nanonets-OCR-s.i1-Q5_K_S.gguf	i1-Q5_K_S	2.17 GB
Nanonets-OCR-s.i1-Q6_K.gguf	i1-Q6_K	2.54 GB
Nanonets-OCR-s.mmproj-Q8_0.gguf	mmproj-Q8_0	845 MB
Nanonets-OCR-s.mmproj-f16.gguf	mmproj-f16	1.34 GB
imatrix.dat	imatrix	3.36 MB