Instructions to use 42ailab/Logics-Parsing-v2-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 42ailab/Logics-Parsing-v2-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 42ailab/Logics-Parsing-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 42ailab/Logics-Parsing-v2-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 42ailab/Logics-Parsing-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 42ailab/Logics-Parsing-v2-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 42ailab/Logics-Parsing-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 42ailab/Logics-Parsing-v2-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 42ailab/Logics-Parsing-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 42ailab/Logics-Parsing-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/42ailab/Logics-Parsing-v2-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use 42ailab/Logics-Parsing-v2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "42ailab/Logics-Parsing-v2-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": "42ailab/Logics-Parsing-v2-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/42ailab/Logics-Parsing-v2-GGUF:Q4_K_M

Ollama
How to use 42ailab/Logics-Parsing-v2-GGUF with Ollama:
```
ollama run hf.co/42ailab/Logics-Parsing-v2-GGUF:Q4_K_M
```
Unsloth Desktop

How to use 42ailab/Logics-Parsing-v2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 42ailab/Logics-Parsing-v2-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": "42ailab/Logics-Parsing-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use 42ailab/Logics-Parsing-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/42ailab/Logics-Parsing-v2-GGUF:Q4_K_M
```

Lemonade

How to use 42ailab/Logics-Parsing-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 42ailab/Logics-Parsing-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Logics-Parsing-v2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use 42ailab/Logics-Parsing-v2-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 42ailab/Logics-Parsing-v2-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 42ailab/Logics-Parsing-v2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use 42ailab/Logics-Parsing-v2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 42ailab/Logics-Parsing-v2-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 "42ailab/Logics-Parsing-v2-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"

Logics-Parsing-v2 · A 4B Document Parser for Formulas, Tables and Flowcharts

English | 简体中文 · Also on ModelScope · 42ailab.com

Give it a page (a book, a paper, a report) and it returns structured HTML: headings, paragraphs, tables and formulas marked separately, flowcharts as Mermaid, sheet music as ABC notation. It runs entirely on your own machine.

The model was built by the Alibaba Logics team and released under Apache-2.0 (Logics-Parsing-v2, based on Qwen3-VL-4B). This repository is neither a new model nor our conversion. The GGUF files were made by community packager mradermacher; this repository is a byte-for-byte backup of them.

Why a backup

The original GGUF lives under a single personal account, on a single host. If it is renamed, deleted or re-uploaded, people who depend on it lose their download, and access to Hugging Face from mainland China is unreliable.

So we keep the original files, unmodified, on ModelScope and on our own Hugging Face account. File names, sizes and sha256 digests all match the original.

What we checked

Byte-identical: every file's sha256 matches mradermacher's repository.

Reproducible: we also converted and quantized the official Alibaba weights ourselves. The quantized data is not byte-identical (different quantizer versions), but on our two test pages (a Chinese book page and an English paper page) both builds produce identical output, layout coordinates included. That confirms the original conversion is sound, and that we could rebuild an equivalent file from the official weights if the original ever disappeared.

This comparison covers two pages. It shows the conversion is correct; it is not an evaluation of the model. For model quality, see the upstream benchmarks (LogicsDocBench, OmniDocBench v1.5).

Use

Packaged for 42model: download Logics-Parsing-v2 from the OCR section of the desktop app, or run 42model download logics-parsing-v2.

Files and license

File	Role	Size
`Logics-Parsing-v2.Q4_K_M.gguf`	text decoder (default)	2.33 GiB
`Logics-Parsing-v2.Q5_K_M.gguf`	text decoder	2.69 GiB
`Logics-Parsing-v2.Q6_K.gguf`	text decoder	3.08 GiB
`Logics-Parsing-v2.Q8_0.gguf`	text decoder	3.99 GiB
`Logics-Parsing-v2.mmproj-f16.gguf`	vision encoder (shared)	0.78 GiB

Weights are under the upstream Apache-2.0 license. Credit for the GGUF conversion goes to mradermacher.

Downloads last month: 69

GGUF

Model size

4B params

Architecture

qwen3vl

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for 42ailab/Logics-Parsing-v2-GGUF

Base model

Logics-MLLM/Logics-Parsing-v2

Quantized

(4)

this model