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

Hermes Agent new

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

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

Atomic Chat

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

dougvk

Add files using upload-large-folder tool

0215d42 verified 4 days ago

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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:

0804568be9f099d6479fad9ed77a4da4611f3c1e7bc6e009af7dce45e8aa3847

Commands

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

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.