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

aaardpark commited on Apr 12

Commit

5152fbc

verified ·

1 Parent(s): d8adc14

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +10 -15

README.md CHANGED Viewed

@@ -12,7 +12,7 @@ quantized_by: aaardpark
 # Qwen2.5-72B-Instruct — GGUF (aaardpark)
-**35 GB Q3_K_M GGUF. 88% GSM8K, where standard 3-bit quantization (RTN) gets 16% at the same size.**
 > Looking for a smaller version? See [aaardpark/Qwen2.5-32B-Instruct-GGUF](https://huggingface.co/aaardpark/Qwen2.5-32B-Instruct-GGUF) — 15 GB, fits on a 24 GB machine.
@@ -56,12 +56,12 @@ You are a helpful assistant.<|im_end|>
 ### Base model evaluation (lm-evaluation-harness)
-| Metric | FP16 | This Quant (3-bit) | RTN 3-bit |
-|--------|------|---------------------|-----------|
-| **Perplexity** (wikitext-2) | 2.670 | **3.163** | 3.750 |
-| **GSM8K** (5-shot) | 90% | **88%** | 16% |
-| **MMLU avg** (5-shot) | 77.6% | **76.8%** | 73.0% |
-| TruthfulQA | 58.5% | 56.9% | 56.3% |
 Measured on Qwen2.5-72B (base) with lm-evaluation-harness. The quantization method is identical for base and Instruct variants.
@@ -79,20 +79,15 @@ Measured on Qwen2.5-72B (base) with lm-evaluation-harness. The quantization meth
 |--------|------|-----------|-------|-------|
 | FP16 | 16 | 2.670 | 90% | Baseline |
 | **This quant** | **3** | **3.163** | **88%** | **35 GB** |
-| RTN 3-bit | 3 | 3.750 | 16% | Standard rounding |
-| GPTQ 4-bit | 4 | 3.562* | — | 25% larger file |
 | RTN 4-bit | 4 | 2.790 | 88% | 45 GB |
 | **This quant (4-bit)** | **4** | **2.747** | **93%** | **Effectively lossless** |
-*GPTQ 4-bit PPL from Qwen2.5-32B (3.562), scaled comparison.
-On smaller models (7B): GPTQ 3-bit PPL = 12.576, our 3-bit PPL = 6.148. GPTQ is unusable at 3-bit; ours is not.
 ## Why this quant is different
-Standard 3-bit quantization (RTN) rounds each weight to the nearest grid point uniformly. This destroys the precise weight values that control multi-step reasoning — GSM8K drops from 90% to 16%.
-Our method uses calibration data to identify which weights are critical for model quality, then allocates quantization precision accordingly. Same bit budget, dramatically different quality.
 ## Which file should I choose?

 # 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](https://huggingface.co/aaardpark/Qwen2.5-32B-Instruct-GGUF) — 15 GB, fits on a 24 GB machine.
 ### 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.
 |--------|------|-----------|-------|-------|
 | 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?