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 / README.md

prithivMLmods

Update README.md

7c6e990 verified about 3 hours ago

preview code

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metadata

library_name: transformers
base_model:
  - datalab-to/chandra-ocr-2
license: openrail
license_link: https://huggingface.co/datalab-to/chandra-ocr-2/blob/main/LICENSE
tags:
  - llama-cpp
  - mtp
  - ocr
  - pdf
  - markdown
  - layout
  - text-generation-inference
  - llama-cli
  - repack
  - HTML
  - JSON
language:
  - en
pipeline_tag: image-text-to-text

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.

Snapshot(3)

llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp

MTP-repackaged and quantized using llama.cpp by — prithivMLmods.