Instructions to use dougvk/chandra-ocr-2-BF16-GGUF-RDNA4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use dougvk/chandra-ocr-2-BF16-GGUF-RDNA4 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 dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16
# Run inference directly in the terminal:
llama cli -hf dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16
# Run inference directly in the terminal:
llama cli -hf dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16

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 dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16
# Run inference directly in the terminal:
./llama-cli -hf dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16

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 dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16

Use Docker

docker model run hf.co/dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16

LM Studio
Jan

vLLM

How to use dougvk/chandra-ocr-2-BF16-GGUF-RDNA4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dougvk/chandra-ocr-2-BF16-GGUF-RDNA4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dougvk/chandra-ocr-2-BF16-GGUF-RDNA4",
		"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/dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16

Ollama
How to use dougvk/chandra-ocr-2-BF16-GGUF-RDNA4 with Ollama:
```
ollama run hf.co/dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16
```

Unsloth Studio

How to use dougvk/chandra-ocr-2-BF16-GGUF-RDNA4 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 dougvk/chandra-ocr-2-BF16-GGUF-RDNA4 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 dougvk/chandra-ocr-2-BF16-GGUF-RDNA4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dougvk/chandra-ocr-2-BF16-GGUF-RDNA4 to start chatting

How to use dougvk/chandra-ocr-2-BF16-GGUF-RDNA4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use dougvk/chandra-ocr-2-BF16-GGUF-RDNA4 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16

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 "dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16" \
  --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"

Docker Model Runner
How to use dougvk/chandra-ocr-2-BF16-GGUF-RDNA4 with Docker Model Runner:
```
docker model run hf.co/dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16
```

Lemonade

How to use dougvk/chandra-ocr-2-BF16-GGUF-RDNA4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16

Run and chat with the model

lemonade run user.chandra-ocr-2-BF16-GGUF-RDNA4-BF16

List all available models

lemonade list

Hermes Agent

How to use dougvk/chandra-ocr-2-BF16-GGUF-RDNA4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16

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 dougvk/chandra-ocr-2-BF16-GGUF-RDNA4:BF16

Run Hermes

hermes

Atomic Chat

chandra-ocr-2-BF16-GGUF-RDNA4 / CONVERSION.md

dougvk

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	# Reproducing the conversion

	These instructions reproduce the publication inputs from pinned upstream revisions. They intentionally download the
	original checkpoint from Hugging Face and do not bypass gated access or licensing.

	## Inputs

	- `datalab-to/chandra-ocr-2` at `af93b47dba1b47b6640c86ccf487ed2260ab9a09`
	- `ggml-org/llama.cpp` at `8f5ab832ca7d8a7b4f23687693fb8b0ecbc227e7`
	- Python 3.12

	The pinned source `model.safetensors` must hash to:

	```text
	0804568be9f099d6479fad9ed77a4da4611f3c1e7bc6e009af7dce45e8aa3847
	```

	## Commands

	```bash
	git clone https://github.com/ggml-org/llama.cpp.git
	git -C llama.cpp checkout --detach 8f5ab832ca7d8a7b4f23687693fb8b0ecbc227e7

	python3.12 -m venv llama.cpp/.venv-convert
	llama.cpp/.venv-convert/bin/pip install \
	-r llama.cpp/requirements/requirements-convert_hf_to_gguf.txt \
	'huggingface_hub[cli]'

	llama.cpp/.venv-convert/bin/hf download datalab-to/chandra-ocr-2 \
	--revision af93b47dba1b47b6640c86ccf487ed2260ab9a09 \
	--local-dir chandra-ocr-2-source

	sha256sum chandra-ocr-2-source/model.safetensors

	llama.cpp/.venv-convert/bin/python llama.cpp/convert_hf_to_gguf.py \
	chandra-ocr-2-source \
	--outfile chandra-ocr-2.BF16.gguf \
	--outtype bf16 \
	--no-mtp

	llama.cpp/.venv-convert/bin/python llama.cpp/convert_hf_to_gguf.py \
	chandra-ocr-2-source \
	--outfile chandra-ocr-2.mmproj-bf16.gguf \
	--outtype bf16 \
	--mmproj

	sha256sum chandra-ocr-2.BF16.gguf chandra-ocr-2.mmproj-bf16.gguf
	```

	The converter can add its `mmproj-` prefix depending on the exact output name. Rename the generated projector to
	`chandra-ocr-2.mmproj-bf16.gguf` before comparing it with the published manifest.

	## Expected outputs

	```text
	4e9d5fa9854cf820d4425d28034df31ec1221a7f9d1082b0c4359d79f318cb56 chandra-ocr-2.BF16.gguf
	54ddb8285933512cdbf1c84238aa0435b473a6efef2caeda8ca802c2899e87b3 chandra-ocr-2.mmproj-bf16.gguf
	```

	If the hashes differ, retain the new converter revision, complete command line, source revision, and resulting hashes
	instead of relabeling the output as this build.