Instructions to use aman2024/NuExtract-2-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aman2024/NuExtract-2-2B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="aman2024/NuExtract-2-2B-GGUF",
	filename="NuExtract-2-2B-IQ3_S.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use aman2024/NuExtract-2-2B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf aman2024/NuExtract-2-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf aman2024/NuExtract-2-2B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf aman2024/NuExtract-2-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf aman2024/NuExtract-2-2B-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 aman2024/NuExtract-2-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf aman2024/NuExtract-2-2B-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 aman2024/NuExtract-2-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aman2024/NuExtract-2-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/aman2024/NuExtract-2-2B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use aman2024/NuExtract-2-2B-GGUF with Ollama:
```
ollama run hf.co/aman2024/NuExtract-2-2B-GGUF:Q4_K_M
```

Unsloth Studio

How to use aman2024/NuExtract-2-2B-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 aman2024/NuExtract-2-2B-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 aman2024/NuExtract-2-2B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aman2024/NuExtract-2-2B-GGUF to start chatting

Docker Model Runner
How to use aman2024/NuExtract-2-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/aman2024/NuExtract-2-2B-GGUF:Q4_K_M
```

Lemonade

How to use aman2024/NuExtract-2-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aman2024/NuExtract-2-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.NuExtract-2-2B-GGUF-Q4_K_M

List all available models

lemonade list

NuExtract-2-2B-GGUF Model Repository

This repository contains the GGUF (GGML Universal Format) versions of the NuMind/NuExtract-2.0-2B model, ready for use with llama.cpp and other GGUF-compatible tools.

These files were generated using the latest convert_hf_to_gguf.py and llama-quantize tools from the llama.cpp repository.

Original Model Information

Original HF Repo: NuMind/NuExtract-2.0-2B
Base Model: Based on the Qwen2-VL-2B-Instruct architecture.
Description: NuExtract 2.0 is a powerful, multilingual family of models specialized for structured information extraction from various sources, including images.

This GGUF conversion allows the model to run efficiently on a wide range of consumer hardware (CPU and GPU).

Provided Files & Quantization Details

This repository offers multiple quantization levels to suit different hardware and performance needs. Quantization reduces model size and memory usage, often with a minimal impact on quality. The "K-Quants" (_K_) are generally recommended over the older quant types.

File Name	Quantization Method	Size	Notes
`NuExtract-2-2B-Q4_K_M.gguf`	`Q4_K_M`	1.1 GB	Balanced Default. The best all-around choice for quality, speed, and size.
`NuExtract-2-2B-Q5_K_M.gguf`	`Q5_K_M`	1.3 GB	High Quality. A great balance, noticeably better than 4-bit. Recommended if you have >2GB VRAM.
`NuExtract-2-2B-Q6_K.gguf`	`Q6_K`	1.5 GB	Very High Quality. Excellent quality with a significant size reduction over 8-bit.
`NuExtract-2-2B-Q8_0.gguf`	`Q8_0`	1.9 GB	Highest Quality. Nearly lossless. Use for benchmarks or if you want the best possible output.
`NuExtract-2-2B-IQ3_S.gguf`	`IQ3_S`	848 MB	Good Compression. A smart 3-bit quant for memory-constrained systems.
`NuExtract-2-2B-Q3_K_M.gguf`	`Q3_K_M`	920 MB	A good alternative 3-bit quant.
`NuExtract-2-2B-Q2_K.gguf`	`Q2_K`	737 MB	Maximum Compression. Very small size, but expect a significant drop in quality.
`NuExtract-2-2B-experimental-fp16.gguf`	`F16`	3.6 GB	Unquantized. Full 16-bit precision. For developers who wish to perform their own quantization.

Note: Older quant types (Q4_0, Q5_0, etc.) are also provided but the _K and IQ versions are generally superior.

How to Use

You can use these models with any program that supports GGUF, such as llama.cpp, Ollama, LM Studio, and many others.

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GGUF

Model size

2B params

Architecture

internlm2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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