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

Use Docker

docker model run hf.co/liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use liodon-ai/ReaderLM-v2-imatrix-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M

Ollama
How to use liodon-ai/ReaderLM-v2-imatrix-GGUF with Ollama:
```
ollama run hf.co/liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M
```

Unsloth Studio

How to use liodon-ai/ReaderLM-v2-imatrix-GGUF 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 liodon-ai/ReaderLM-v2-imatrix-GGUF 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 liodon-ai/ReaderLM-v2-imatrix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for liodon-ai/ReaderLM-v2-imatrix-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use liodon-ai/ReaderLM-v2-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M
```

Lemonade

How to use liodon-ai/ReaderLM-v2-imatrix-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.ReaderLM-v2-imatrix-GGUF-Q4_K_M

List all available models

lemonade list

ReaderLM-v2 — iMatrix GGUF

GGUF quantizations of jinaai/ReaderLM-v2, published by Liodon AI.

Quick Start

llama.cpp

llama-cli -hf liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M

Ollama

ollama run hf.co/liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M

LM Studio / Jan — search liodon-ai/ReaderLM-v2-imatrix-GGUF and pick your quant.

Quants

Quant	Size	VRAM est.	Notes
`IQ2_M`	0.60 GB	~1 GB	2-bit, iMatrix — smallest usable
`IQ3_M`	0.78 GB	~1 GB	3-bit, iMatrix — great quality/size tradeoff
`IQ4_XS`	0.90 GB	~1 GB	4-bit extra-small, iMatrix
`Q4_K_M`	0.99 GB	~1 GB	4-bit, iMatrix-calibrated (recommended)
`Q5_K_M`	1.13 GB	~1 GB	5-bit, iMatrix-calibrated
`Q6_K`	1.27 GB	~1 GB	6-bit, iMatrix-calibrated, near-lossless
`Q8_0`	1.65 GB	~2 GB	8-bit, essentially lossless

What is iMatrix?

Standard quantization treats all weights equally. iMatrix runs 128 calibration chunks through the full-precision model to find which weights matter most, then allocates more precision where it counts. At Q2/Q3/Q4 this means noticeably better coherence and instruction-following — same file size, better output.

Calibration: 2M tokens of WikiText-103.

Also see plain (non-iMatrix) quants: liodon-ai/ReaderLM-v2-GGUF

Source

Model: jinaai/ReaderLM-v2
License: other

Quantized by Liodon AI

Downloads last month: 243

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for liodon-ai/ReaderLM-v2-imatrix-GGUF

Base model

jinaai/ReaderLM-v2

Quantized

(37)

this model