Instructions to use manateelazycat/ReaderLM-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 manateelazycat/ReaderLM-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 manateelazycat/ReaderLM-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf manateelazycat/ReaderLM-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 manateelazycat/ReaderLM-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf manateelazycat/ReaderLM-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 manateelazycat/ReaderLM-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf manateelazycat/ReaderLM-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 manateelazycat/ReaderLM-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf manateelazycat/ReaderLM-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/manateelazycat/ReaderLM-v2-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use manateelazycat/ReaderLM-v2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "manateelazycat/ReaderLM-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": "manateelazycat/ReaderLM-v2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/manateelazycat/ReaderLM-v2-GGUF:Q4_K_M

Ollama
How to use manateelazycat/ReaderLM-v2-GGUF with Ollama:
```
ollama run hf.co/manateelazycat/ReaderLM-v2-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use manateelazycat/ReaderLM-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/manateelazycat/ReaderLM-v2-GGUF:Q4_K_M
```

Lemonade

How to use manateelazycat/ReaderLM-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull manateelazycat/ReaderLM-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ReaderLM-v2-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

ReaderLM-v2 Q4_K_M for Jetson

This repository mirrors the tested GGUF Q4_K_M conversion by mradermacher, from https://huggingface.co/mradermacher/ReaderLM-v2-GGUF at revision 08a86568ae97ff37fbc22688ac919be29f5bca3e. Original model: Jina AI ReaderLM-v2. No weight bytes were changed.

The file SHA-256 is d5bf01392bc58fc49f15d9441b16969da388c6601b9c192d4a6a1d8f31be2512.

Tested on AGX Orin JetPack 6.2 with pinned llama.cpp 970b51ce7c9ba5fff0726c5e9994f1c2f0e4eedc: Q4_K_M weights, f16 KV, Flash Attention, CUDA Graph, 32K total input/output capacity, one slot, --cache-ram 0. A synthetic short HTML extraction decoded about 81 tokens/s. This is a warm workload measurement, not an accuracy guarantee or full-page throughput limit.

ReaderLM is specialized for HTML-to-Markdown and schema-guided JSON extraction. It is not a general-purpose chat recommendation. The deployed 32K limit is smaller than the upstream model maximum.

License / 许可证

CC-BY-NC-4.0. Preserve Jina AI attribution and the original license. 非商业许可；商业使用请向 Jina AI 获取授权。See NOTICE.md.

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GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for manateelazycat/ReaderLM-v2-GGUF

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jinaai/ReaderLM-v2

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