Instructions to use maximg/AutoRoundTest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maximg/AutoRoundTest with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="maximg/AutoRoundTest",
	filename="Qwen3.6-27B-Q5_K_M_AutoRound_M2.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 maximg/AutoRoundTest 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 maximg/AutoRoundTest:Q5_K_M_AUTOROUND
# Run inference directly in the terminal:
llama cli -hf maximg/AutoRoundTest:Q5_K_M_AUTOROUND

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf maximg/AutoRoundTest:Q5_K_M_AUTOROUND
# Run inference directly in the terminal:
llama cli -hf maximg/AutoRoundTest:Q5_K_M_AUTOROUND

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 maximg/AutoRoundTest:Q5_K_M_AUTOROUND
# Run inference directly in the terminal:
./llama-cli -hf maximg/AutoRoundTest:Q5_K_M_AUTOROUND

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 maximg/AutoRoundTest:Q5_K_M_AUTOROUND
# Run inference directly in the terminal:
./build/bin/llama-cli -hf maximg/AutoRoundTest:Q5_K_M_AUTOROUND

Use Docker

docker model run hf.co/maximg/AutoRoundTest:Q5_K_M_AUTOROUND

LM Studio
Jan

Ollama

How to use maximg/AutoRoundTest with Ollama:

ollama run hf.co/maximg/AutoRoundTest:Q5_K_M_AUTOROUND

Unsloth Studio

How to use maximg/AutoRoundTest 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 maximg/AutoRoundTest 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 maximg/AutoRoundTest to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for maximg/AutoRoundTest to start chatting

How to use maximg/AutoRoundTest with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf maximg/AutoRoundTest:Q5_K_M_AUTOROUND

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": "maximg/AutoRoundTest:Q5_K_M_AUTOROUND"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use maximg/AutoRoundTest with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf maximg/AutoRoundTest:Q5_K_M_AUTOROUND

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 maximg/AutoRoundTest:Q5_K_M_AUTOROUND

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use maximg/AutoRoundTest with Docker Model Runner:
```
docker model run hf.co/maximg/AutoRoundTest:Q5_K_M_AUTOROUND
```

Lemonade

How to use maximg/AutoRoundTest with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull maximg/AutoRoundTest:Q5_K_M_AUTOROUND

Run and chat with the model

lemonade run user.AutoRoundTest-Q5_K_M_AUTOROUND

List all available models

lemonade list

AutoRoundTest / README.md

maximg

Update README.md

e8b80af verified 16 days ago

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	---
	license: apache-2.0
	language:
	- multilingual
	base_model: Qwen/Qwen3.6-27B
	tags:
	- auto-round
	- intel
	- gguf
	- quantization
	---

	# Qwen3.6-27B GGUF (AutoRound Quantized, MTP Enabled)

	This repository contains GGUF quantized versions of [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) created using Intel's [AutoRound](https://github.com/intel/auto-round) quantization method.

	## Quantization Details

	The models were generated using Intel's AutoRound using ultrachat_200k as the test dataset and using sequence length of 2850. MTP layers were not explicitly enabled, but it works with MTP for me

	```bash
	auto-round \
	--model Qwen/Qwen3.6-27B \
	--output_dir ./quantized/ \
	--scheme <SCHEME> \
	--format <SCHEME> \
	--iters 0 \
	--nsamples 256 --seqlen 2850 --dataset "HuggingFaceH4/ultrachat_200k"
	```

	For now, only 2 quantization variants were used Q5_K_M and Q4_K_MIXED. Q4_K_MIXED is a custom variant based on Intel's original Q2_K_MIXED quantization, but using Q4_K quants instead of Q2.


	### Files and Sizes

	\| File Name \| Quant Type \| Size \|
	\|-----------\|------------\|------\|
	\| `Qwen3.6-27B-Q2_K_MIXED.gguf` \| Q2_K_MIXED \| 16.5 GB \|
	\| `Qwen3.6-27B-Q5_K_M.gguf` \| Q5_K_M \| 19 GB \|