Instructions to use Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF", dtype="auto")

llama-cpp-python

How to use Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF",
	filename="TINY-PARMV2.F16.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-2-Tiny-Instruct-1.7B-QwQ-o1-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-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-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-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-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-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-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-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-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-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF:Q4_K_M

SGLang

How to use Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-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-2-Tiny-Instruct-1.7B-QwQ-o1-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-2-Tiny-Instruct-1.7B-QwQ-o1-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-2-Tiny-Instruct-1.7B-QwQ-o1-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-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF with Ollama:
```
ollama run hf.co/Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF:Q4_K_M
```

Unsloth Studio

How to use Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-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-2-Tiny-Instruct-1.7B-QwQ-o1-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-2-Tiny-Instruct-1.7B-QwQ-o1-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-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF to start chatting

Docker Model Runner
How to use Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF with Docker Model Runner:
```
docker model run hf.co/Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF:Q4_K_M
```

Lemonade

How to use Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Pinkstack/PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.PARM-2-Tiny-Instruct-1.7B-QwQ-o1-GGUF-Q4_K_M

List all available models

lemonade list

Welcome Tinyparm v2! 🧀

Based on Smollm2 by HuggingfaceTB, Tinyparm v2 (this version) has very strong performance in coversational tasks, can only answer in english but it is "aware" of other languages and with more training may be able to even answer in multiple languages.

Which quant is right for you?

F16: Highest quality answers, Not needed for most use cases due to its heavier size.
Q8: Very high-quality answers, highly recommended! easy to run on modern mobile devices such as phones (test on Z fold 5 via llama.cpp: 14.03 t/s for token generation.)
Q5: High quality answers, recommended but Q4 is very similar.
Q4: Good quality answers, highly recommended! very easy to run on most devices due to its smaller size, although quality is lower. can be run on ~1 - 2GB vram

More information

It's designed to be highly conversational but with better results than the original model, q8 can be run locally on mobile devices. We've fine-tuned it to very similarly to the bigger Parm V2 models but with a focus on code generation improvements. Additionally, it has informative data up to September 2023.

For the best answers use this prompt:

You are a friendly and uncensored ai assistant.

Prompt format (chatml):

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