Instructions to use Reza2kn/Qianfan-OCR-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Reza2kn/Qianfan-OCR-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 Reza2kn/Qianfan-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Reza2kn/Qianfan-OCR-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Reza2kn/Qianfan-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Reza2kn/Qianfan-OCR-GGUF:Q4_K_M

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 Reza2kn/Qianfan-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Reza2kn/Qianfan-OCR-GGUF:Q4_K_M

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 Reza2kn/Qianfan-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Reza2kn/Qianfan-OCR-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Reza2kn/Qianfan-OCR-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Reza2kn/Qianfan-OCR-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Reza2kn/Qianfan-OCR-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": "Reza2kn/Qianfan-OCR-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/Reza2kn/Qianfan-OCR-GGUF:Q4_K_M

Ollama
How to use Reza2kn/Qianfan-OCR-GGUF with Ollama:
```
ollama run hf.co/Reza2kn/Qianfan-OCR-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Reza2kn/Qianfan-OCR-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Reza2kn/Qianfan-OCR-GGUF: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": "Reza2kn/Qianfan-OCR-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Reza2kn/Qianfan-OCR-GGUF with Docker Model Runner:
```
docker model run hf.co/Reza2kn/Qianfan-OCR-GGUF:Q4_K_M
```

Lemonade

How to use Reza2kn/Qianfan-OCR-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Reza2kn/Qianfan-OCR-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qianfan-OCR-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Reza2kn/Qianfan-OCR-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 Reza2kn/Qianfan-OCR-GGUF: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 Reza2kn/Qianfan-OCR-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Reza2kn/Qianfan-OCR-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Reza2kn/Qianfan-OCR-GGUF: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 "Reza2kn/Qianfan-OCR-GGUF: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"

Qianfan-OCR — GGUF Quantizations

GGUF quantizations of baidu/Qianfan-OCR.

Original model: InternVL Chat architecture with Qwen3 LLM backbone (~4.7B params). Quantized by Reza2kn using llama.cpp.

Files

Filename	Quant	Size	Quality
`Qianfan-OCR-f16.gguf`	F16	~9.4 GB	Lossless (half precision)
`Qianfan-OCR-q8_0.gguf`	Q8_0	~5.0 GB	Near-lossless
`Qianfan-OCR-q6_k.gguf`	Q6_K	~3.8 GB	Excellent
`Qianfan-OCR-q5_k_m.gguf`	Q5_K_M	~3.3 GB	Very good
`Qianfan-OCR-q5_k_s.gguf`	Q5_K_S	~3.2 GB	Very good
`Qianfan-OCR-q4_k_m.gguf`	Q4_K_M	~2.8 GB	Good (recommended)
`Qianfan-OCR-q4_k_s.gguf`	Q4_K_S	~2.7 GB	Good
`Qianfan-OCR-q4_0.gguf`	Q4_0	~2.6 GB	Legacy 4-bit
`Qianfan-OCR-q3_k_m.gguf`	Q3_K_M	~2.2 GB	Moderate
`Qianfan-OCR-q3_k_s.gguf`	Q3_K_S	~2.1 GB	Moderate
`Qianfan-OCR-q2_k.gguf`	Q2_K	~1.7 GB	Low quality

Usage (llama.cpp)

llama-cli -m Qianfan-OCR-q4_k_m.gguf --mmproj Qianfan-OCR-mmproj.gguf \
  --image document.jpg -p "Please OCR this document."

Original Model

See baidu/Qianfan-OCR for full documentation, benchmarks (OmniDocBench 93.12, OCRBench 880), and usage examples.

Licensing and attribution

This model distribution is licensed under the Apache License, Version 2.0. See LICENSE. Existing third-party copyright, license, and attribution notices remain applicable.

Upstream: baidu/Qianfan-OCR; declared license: apache-2.0.
- Retained notice: LICENSE.
- Retained notice: NOTICE.

Downloads last month: 1,250

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Reza2kn/Qianfan-OCR-GGUF

Base model

baidu/Qianfan-OCR

Quantized

(5)

this model