Instructions to use aaardpark/Qwen2.5-72B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aaardpark/Qwen2.5-72B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="aaardpark/Qwen2.5-72B-Instruct-GGUF",
	filename="Qwen2.5-72B-Instruct-aaardpark-Q3_K_M.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 aaardpark/Qwen2.5-72B-Instruct-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 aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_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 aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_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 aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_K_M

Use Docker

docker model run hf.co/aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_K_M

LM Studio
Jan
Ollama
How to use aaardpark/Qwen2.5-72B-Instruct-GGUF with Ollama:
```
ollama run hf.co/aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_K_M
```

Unsloth Studio

How to use aaardpark/Qwen2.5-72B-Instruct-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 aaardpark/Qwen2.5-72B-Instruct-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 aaardpark/Qwen2.5-72B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aaardpark/Qwen2.5-72B-Instruct-GGUF to start chatting

How to use aaardpark/Qwen2.5-72B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use aaardpark/Qwen2.5-72B-Instruct-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 aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_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 aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use aaardpark/Qwen2.5-72B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_K_M
```

Lemonade

How to use aaardpark/Qwen2.5-72B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aaardpark/Qwen2.5-72B-Instruct-GGUF:Q3_K_M

Run and chat with the model

lemonade run user.Qwen2.5-72B-Instruct-GGUF-Q3_K_M

List all available models

lemonade list

Qwen2.5-72B-Instruct-GGUF / README.md

aaardpark

Upload README.md with huggingface_hub

5152fbc verified 3 months ago

preview code

Raw

History Blame Contribute Delete

3.58 kB

metadata

license: apache-2.0
base_model: Qwen/Qwen2.5-72B-Instruct
tags:
  - quantized
  - gguf
  - 3-bit
  - qwen2
model_type: qwen2
quantized_by: aaardpark

Qwen2.5-72B-Instruct — GGUF (aaardpark)

35 GB Q3_K_M GGUF. 88% GSM8K at 3-bit.

Looking for a smaller version? See aaardpark/Qwen2.5-32B-Instruct-GGUF — 15 GB, fits on a 24 GB machine.

Quick stats

File	Size	BPW	Min RAM	Speed (M5 Max, Metal)
`Qwen2.5-72B-Instruct-aaardpark-Q3_K_M.gguf`	35 GB	3.9	48 GB	~5 tok/s

How to use

Download

huggingface-cli download aaardpark/Qwen2.5-72B-Instruct-GGUF \
  Qwen2.5-72B-Instruct-aaardpark-Q3_K_M.gguf --local-dir .

Run

llama.cpp:

llama-cli -m Qwen2.5-72B-Instruct-aaardpark-Q3_K_M.gguf -ngl 99 -p "Hello!"

LM Studio: Search for aaardpark/Qwen2.5-72B-Instruct-GGUF in the model browser.

Prompt format

This model uses the ChatML template:

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Benchmarks

Base model evaluation (lm-evaluation-harness)

Metric	FP16	This Quant (3-bit)
Perplexity (wikitext-2)	2.670	3.163
GSM8K (5-shot)	90%	88%
MMLU avg (5-shot)	77.6%	76.8%
TruthfulQA	58.5%	56.9%

Measured on Qwen2.5-72B (base) with lm-evaluation-harness. The quantization method is identical for base and Instruct variants.

GGUF perplexity (wikitext-2, llama.cpp)

Variant	PPL
Base Q8_0 (exact weights)	3.028
Base Q3_K_M (this format)	2.904
Instruct Q3_K_M	3.962

vs other quantization methods

Method	Bits	PPL (72B)	GSM8K	Notes
FP16	16	2.670	90%	Baseline
This quant	3	3.163	88%	35 GB
RTN 3-bit	3	3.750	—	Standard rounding
RTN 4-bit	4	2.790	88%	45 GB
This quant (4-bit)	4	2.747	93%	Effectively lossless

Why this quant is different

Standard 3-bit quantization (RTN) rounds each weight to the nearest grid point uniformly. Our method uses calibration data to identify which weights are critical for model quality, then allocates quantization precision accordingly. Same bit budget, better weight choices.

The result: 88% GSM8K and 76.8% MMLU at 3-bit, within 2 points of FP16 on both benchmarks.

Which file should I choose?

This file is 35 GB. Realistic RAM requirements:

≥64 GB RAM: comfortable, full 128K context window
48 GB RAM: works with 16K-32K context
32 GB RAM: tight, short context only — consider the 32B variant instead
<32 GB RAM: use the 32B variant (15 GB)

On Apple Silicon with Metal offload (-ngl 99), expect ~5 tok/s on M5 Max. NVIDIA GPUs need ~40 GB VRAM for full offload.

Method

Importance-weighted per-group optimization. Calibration data identifies which weights are critical for model quality, then quantization precision is allocated accordingly. ~20 minutes per quant on a single GPU. Output is standard Q3_K_M GGUF format — no custom kernels required.

Group size: 128
GGUF format: Q3_K_M (via llama.cpp)
Context: 128K tokens

Acknowledgments

Built on Qwen/Qwen2.5-72B-Instruct by Alibaba Cloud.