Instructions to use Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF", dtype="auto")

llama-cpp-python

How to use Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF",
	filename="PARM-Qwen2.5-0.5B-o.1-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Pinkstack/PARM-Qwen2.5-o1-0.5B-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 Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Pinkstack/PARM-Qwen2.5-o1-0.5B-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 Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Pinkstack/PARM-Qwen2.5-o1-0.5B-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 Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pinkstack/PARM-Qwen2.5-o1-0.5B-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": "Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF:Q4_K_M

SGLang

How to use Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF with Ollama:
```
ollama run hf.co/Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Pinkstack/PARM-Qwen2.5-o1-0.5B-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 Pinkstack/PARM-Qwen2.5-o1-0.5B-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 Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF to start chatting

How to use Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF:Q4_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": "Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF:Q4_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 Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF with Docker Model Runner:
```
docker model run hf.co/Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF:Q4_K_M
```

Lemonade

How to use Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.PARM-Qwen2.5-o1-0.5B-GGUF-Q4_K_M

List all available models

lemonade list

PRAM V2

🧀 Which quant is right for you?

Q4: This model should be used for super low end devices like older phones or older laptops due to its very compact size, quality is okay but fully usable.
Q6: This model should be used on most modern devices, good quality and very quick responses.
Q8: This model should be used on most modern devices Responses are very high quality, but its a little slower than q6
BF16: This Lossless model should only be used if maximum quality is needed; it doesn't perform well speed wise, but text results are high quality.

Things you should be aware of when using PARM models (Pinkstack Accuracy Reasoning Models) 🧀

This PARM is based on Qwen 2.5 0.5B which has gotten extra reasoning training parameters so it would have similar outputs to qwen QwQ (only much, smaller.), We trained with this dataset. it is designed to run on any device, from your phone to high-end PC. that is why we've included a BF16 quant.

To use this model, you must use a service which supports the GGUF file format. Additionaly, this is the Prompt Template, it uses the qwen2 template.

{{{ if .System }}<|system|>
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|user|>
{{ .Prompt }}<|im_end|>
{{ end }}<|assistant|>
{{ .Response }}<|im_end|>

Or if you are using an anti prompt: <|end|><|assistant|>

Highly recommended to use with a system prompt.

Extra information

Developed by: Pinkstack
License: apache-2.0
Finetuned from model : unsloth/qwen2.5-0.5b-instruct-bnb-4bit

This model was trained using Unsloth and Huggingface's TRL library.

Used this model? Don't forget to leave a like :)

Downloads last month: 114

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

4-bit

6-bit

8-bit

16-bit

Collection including Pinkstack/PARM-Qwen2.5-o1-0.5B-GGUF

Parm V1

Collection

First gen of the Pinkstack advanced reasoning models. Medium quality but better than the original models they are based on. • 3 items • Updated Sep 22, 2025 • 2