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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="benhaotang/Nanonets-OCR-s-GGUF",
	filename="Nanonets-OCR-s-F16.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 benhaotang/Nanonets-OCR-s-GGUF with llama.cpp:

Install from brew

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

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

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

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

Unsloth Studio new

How to use benhaotang/Nanonets-OCR-s-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 benhaotang/Nanonets-OCR-s-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 benhaotang/Nanonets-OCR-s-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Quatized nanonets/Nanonets-OCR-s with llama.cpp commit fb85a288

Multiple ways to use:

run with llama.cpp: ./llama-server -m "Nanonets-OCR-s-Q4_K_M.gguf" --mmproj "mmproj-Nanonets-OCR-s.gguf"
use with lmstudio: just pull from benhaotang/Nanonets-OCR-s-GGUF (Warning‼️: change chat template to chatml in model settings)
use with ollama: ollama run benhaotang/Nanonets-OCR-s

Suggested system prompt:

Extract the text from the above document as if you were reading it naturally.
Return the tables in html format. Return the equations in LaTeX representation.
If there is an image in the document and image caption is not present,
add a small description of the image inside the <img></img> tag;
otherwise, add the image caption inside <img></img>.
Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number> or <page_number>9/22</page_number>.
Prefer using ☐ and ☑ for check boxes.

Downloads last month: 90

GGUF

Model size

3B params

Architecture

qwen2vl

Hardware compatibility

4-bit

6-bit

8-bit

16-bit

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Model tree for benhaotang/Nanonets-OCR-s-GGUF

Base model

Qwen/Qwen2.5-VL-3B-Instruct

Finetuned

nanonets/Nanonets-OCR-s

Quantized

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