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

Libraries

How to use prithivMLmods/chandra-ocr-2-MTP-Repacked 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-MTP-Repacked")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("prithivMLmods/chandra-ocr-2-MTP-Repacked")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/chandra-ocr-2-MTP-Repacked", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/chandra-ocr-2-MTP-Repacked 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-MTP-Repacked:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/chandra-ocr-2-MTP-Repacked:Q4_K_M

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-MTP-Repacked:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/chandra-ocr-2-MTP-Repacked:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf prithivMLmods/chandra-ocr-2-MTP-Repacked:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf prithivMLmods/chandra-ocr-2-MTP-Repacked:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/chandra-ocr-2-MTP-Repacked:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use prithivMLmods/chandra-ocr-2-MTP-Repacked 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-MTP-Repacked"
# 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-MTP-Repacked",
		"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-MTP-Repacked:Q4_K_M

SGLang

How to use prithivMLmods/chandra-ocr-2-MTP-Repacked 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-MTP-Repacked" \
    --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-MTP-Repacked",
		"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-MTP-Repacked" \
        --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-MTP-Repacked",
		"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-MTP-Repacked with Ollama:
```
ollama run hf.co/prithivMLmods/chandra-ocr-2-MTP-Repacked:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/chandra-ocr-2-MTP-Repacked 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-MTP-Repacked:Q4_K_M

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-MTP-Repacked:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/chandra-ocr-2-MTP-Repacked with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/chandra-ocr-2-MTP-Repacked:Q4_K_M
```

Lemonade

How to use prithivMLmods/chandra-ocr-2-MTP-Repacked with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/chandra-ocr-2-MTP-Repacked:Q4_K_M

Run and chat with the model

lemonade run user.chandra-ocr-2-MTP-Repacked-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/chandra-ocr-2-MTP-Repacked 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-MTP-Repacked:Q4_K_M

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-MTP-Repacked:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/chandra-ocr-2-MTP-Repacked 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-MTP-Repacked:Q4_K_M

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-MTP-Repacked:Q4_K_M" \
  --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-MTP-Repacked

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: Yes | GGUF — chandra-ocr-2-MTP-Repacked(GGUF)

Multi-Token Prediction (MTP) GGUF is a specialized GGUF model file format extension that integrates speculative decoding directly into the model weights to significantly accelerate local inference. Unlike traditional speculative decoding which requires a separate, smaller "draft" model, MTP GGUF files include additional output heads within the main model architecture that predict multiple future tokens in a single forward pass.

Model Files

File Name	Quant Type	File Size	File Link	Description
chandra-ocr-2-MTP.BF16.gguf	BF16	8.67 GB	Link	Full BF16 weights. Highest quality, largest file size.
chandra-ocr-2-MTP.Q3_K_L.gguf	Q3_K_L	2.48 GB	Link	Lower quality but usable, good for low RAM availability.
chandra-ocr-2-MTP.Q3_K_M.gguf	Q3_K_M	2.32 GB	Link	Low quality.
chandra-ocr-2-MTP.Q4_K_M.gguf	Q4_K_M	2.78 GB	Link	Good quality, default size for most use cases, recommended.
chandra-ocr-2-MTP.Q4_K_S.gguf	Q4_K_S	2.63 GB	Link	Slightly lower quality with more space savings, recommended.
chandra-ocr-2-MTP.Q5_K_M.gguf	Q5_K_M	3.16 GB	Link	High quality, recommended.
chandra-ocr-2-MTP.Q5_K_S.gguf	Q5_K_S	3.07 GB	Link	High quality, recommended.
chandra-ocr-2-MTP.Q6_K.gguf	Q6_K	3.56 GB	Link	Very high quality, near perfect, recommended.
chandra-ocr-2-MTP.Q8_0.gguf	Q8_0	4.61 GB	Link	Extremely high quality, generally unneeded but max available quant.
chandra-ocr-2-MTP.mmproj-bf16.gguf	mmproj-bf16	676 MB	Link	Multimodal projection file in BF16 format. Used for vision/OCR capabilities.

MTP-Repacked [Transformers, BF16]

File Name	File Size	File Link	Description
chat_template.jinja	7.62 kB	Link	Jinja template for chat formatting.
config.json	2.88 kB	Link	Model configuration file.
generation_config.json	116 Bytes	Link	Generation configuration parameters.
model-00001-of-00002.safetensors	5.07 GB	Link	Model weights shard 1 of 2 (Safetensors format).
model-00002-of-00002.safetensors	4.25 GB	Link	Model weights shard 2 of 2 (Safetensors format).
model.safetensors.index.json	67.3 kB	Link	Index file mapping weights to shards.
processor_config.json	1.33 kB	Link	Processor configuration for multimodal inputs.
tokenizer.json	20 MB	Link	Tokenizer vocabulary and merges.
tokenizer_config.json	1.51 kB	Link	Tokenizer configuration file.

BF16 Test Run

Tested chandra-ocr-2-MTP.BF16.gguf (with chandra-ocr-2-MTP.mmproj-bf16.gguf) on an NVIDIA RTX PRO 6000 Blackwell Server Edition GPU with 96 GB of VRAM, using llama-cli with full GPU offloading and MTP speculative decoding.

Performance Metrics

Metric	Value
Average Prompt Processing Speed	2,383.63 tokens/s
Average Generation Speed	196.98 tokens/s
Total Wall Time	25.4 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	10.66 GiB
Peak GPU Utilization	87%
GPU Layers Offloaded	99 (`-ngl 99`)
Random Seed	42
Speculative Decoding	MTP (`draft_n_max=2`)
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

./build/bin/llama-cli \
  -m /path/to/models/chandra-ocr-2-MTP.BF16.gguf \
  --mmproj /path/to/models/chandra-ocr-2-MTP.mmproj-bf16.gguf \
  --image /path/to/image.jpeg \
  --spec-type draft-mtp \
  --spec-draft-n-max 2 \
  -ngl 99 \
  -c 16384 \
  --cache-type-k q8_0 \
  --seed 42 \
  -n 8192 \
  -st \
  -p "OCR the image."

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