Instructions to use vnyaryan/playwright5model_q4_k_m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vnyaryan/playwright5model_q4_k_m 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 vnyaryan/playwright5model_q4_k_m:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vnyaryan/playwright5model_q4_k_m:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vnyaryan/playwright5model_q4_k_m:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vnyaryan/playwright5model_q4_k_m: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 vnyaryan/playwright5model_q4_k_m:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vnyaryan/playwright5model_q4_k_m: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 vnyaryan/playwright5model_q4_k_m:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vnyaryan/playwright5model_q4_k_m:Q4_K_M

Use Docker

docker model run hf.co/vnyaryan/playwright5model_q4_k_m:Q4_K_M

LM Studio
Jan

vLLM

How to use vnyaryan/playwright5model_q4_k_m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vnyaryan/playwright5model_q4_k_m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vnyaryan/playwright5model_q4_k_m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vnyaryan/playwright5model_q4_k_m:Q4_K_M

Ollama
How to use vnyaryan/playwright5model_q4_k_m with Ollama:
```
ollama run hf.co/vnyaryan/playwright5model_q4_k_m:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use vnyaryan/playwright5model_q4_k_m with Docker Model Runner:
```
docker model run hf.co/vnyaryan/playwright5model_q4_k_m:Q4_K_M
```

Lemonade

How to use vnyaryan/playwright5model_q4_k_m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vnyaryan/playwright5model_q4_k_m:Q4_K_M

Run and chat with the model

lemonade run user.playwright5model_q4_k_m-Q4_K_M

List all available models

lemonade list

Atomic Chat

Playwright1 GGUF Model

This model is a 4-bit LoRA fine-tuned version of unsloth/Llama-3.2-3B-Instruct, optimized for conversational instruction-following tasks. Trained on custom command-response data using the ShareGPT format.

Features

🧠 Fine-tuned with LoRA (r=16) using Unsloth
💾 Quantized to 4-bit (q4_k_m) for fast inference
🔧 Ideal for lightweight deployment

Training Info

Trained with SFTTrainer (TRL) for 60 steps with 2 batch size on Google Colab.

License

Apache 2.0

Downloads last month: 13

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit