Instructions to use prithivMLmods/chandra-ocr-2-NVFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/chandra-ocr-2-NVFP4-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/chandra-ocr-2-NVFP4-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/chandra-ocr-2-NVFP4-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/chandra-ocr-2-NVFP4-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 prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4

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/chandra-ocr-2-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4

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/chandra-ocr-2-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4

Llama new
LM Studio
Jan

vLLM

How to use prithivMLmods/chandra-ocr-2-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/chandra-ocr-2-NVFP4-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/chandra-ocr-2-NVFP4-GGUF",
		"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/prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4

SGLang

How to use prithivMLmods/chandra-ocr-2-NVFP4-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/chandra-ocr-2-NVFP4-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/chandra-ocr-2-NVFP4-GGUF",
		"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 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/chandra-ocr-2-NVFP4-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/chandra-ocr-2-NVFP4-GGUF",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/chandra-ocr-2-NVFP4-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4
```
Unsloth Desktop

How to use prithivMLmods/chandra-ocr-2-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/chandra-ocr-2-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4
```

Lemonade

How to use prithivMLmods/chandra-ocr-2-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.chandra-ocr-2-NVFP4-GGUF-NVFP4

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/chandra-ocr-2-NVFP4-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/chandra-ocr-2-NVFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "prithivMLmods/chandra-ocr-2-NVFP4-GGUF:NVFP4" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

chandra-ocr-2-NVFP4-GGUF

Chandra-OCR-2 from Datalab is a state-of-the-art OCR model that outputs structured markdown, HTML, or JSON while preserving precise layout information from images and PDFs across 90+ languages. It achieves SOTA benchmarks with 85.9% on olmocr and 77.8% multilingual score (+12% over Chandra 1), delivering major gains in math equation parsing, complex table reconstruction (including merged cells), handwriting recognition, form elements like checkboxes, and wide-document layouts alongside vastly improved image captioning and diagram extraction. Available via free playground, hosted API for production speed/accuracy, or local deployment through HuggingFace Transformers/vLLM, it excels at transforming challenging real-world documents—financial filings, research papers, historical scans, multilingual forms—into semantically rich structured data for downstream AI pipelines and automation workflows.

MTP — Speculative Decoding: No

Model Files

File Name	Quant Type	File Size	File Link	Description
chandra-ocr-2-NVFP4.gguf	NVFP4	4.75 GB	Link	NVFP4 quantization. Highly optimized for specific hardware accelerators, offering a strong balance of size and performance.
chandra-ocr-2.mmproj.gguf	mmproj	676 MB	Link	Multimodal projection file. Used for vision/language models.

NVFP4 Test Run

Tested chandra-ocr-2-NVFP4.gguf with multimodal vision support using llama-cli on an NVIDIA RTX PRO 6000 Blackwell Server Edition GPU with 96 GB of VRAM.

Performance Metrics

Metric	Value
Average Prompt Processing Speed	2,957.14 tokens/s
Average Generation Speed	192.13 tokens/s
Average Wall Time	8.98 seconds
Total Wall Time	26.9 seconds
Context Length	16,384 tokens
KV Cache (K)	`q8_0`
GPU	NVIDIA RTX PRO 6000 Blackwell Server Edition
GPU VRAM Capacity	96 GB
Peak GPU VRAM Usage	6.66 GiB
Peak GPU Utilization	88%
GPU Layers Offloaded	99 (`-ngl 99`)
Random Seed	42
Speculative Decoding	Disabled
Vision Support	Enabled (`--mmproj`)
Test Runs	3

Test Shots

Shot	Test
Shot 1	Vision OCR: Image 1
Shot 2	Vision OCR: Image 2
Shot 3	Vision OCR: Image 3

Run Inference

# Download the image
wget -O /path/to/models/input.jpeg \
  "https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/EF8cldKHhUEO3MZPZSD19.jpeg"

# Run Chandra OCR 2 NVFP4 with vision support
/data/llama.cpp/build/bin/llama-cli \
  -m /path/to/models/chandra-ocr-2-NVFP4.gguf \
  --mmproj /path/to/models/chandra-ocr-2.mmproj.gguf \
  -ngl 99 \
  -c 16384 \
  --cache-type-k q8_0 \
  --seed 42 \
  -n 8192 \
  -st \
  --image /path/to/models/input.jpeg \
  -p "OCR the image"

Performance results reflect this specific hardware and inference configuration. Actual throughput may vary depending on the image, model, software build, and runtime settings.