Instructions to use playable/Playable1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use playable/Playable1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="playable/Playable1-GGUF",
	filename="Playable1-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use playable/Playable1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf playable/Playable1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf playable/Playable1-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 playable/Playable1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf playable/Playable1-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 playable/Playable1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf playable/Playable1-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 playable/Playable1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf playable/Playable1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/playable/Playable1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use playable/Playable1-GGUF with Ollama:
```
ollama run hf.co/playable/Playable1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use playable/Playable1-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 playable/Playable1-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 playable/Playable1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for playable/Playable1-GGUF to start chatting

Pi new

How to use playable/Playable1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf playable/Playable1-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": "Playable1-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use playable/Playable1-GGUF with Docker Model Runner:
```
docker model run hf.co/playable/Playable1-GGUF:Q4_K_M
```

Lemonade

How to use playable/Playable1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull playable/Playable1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Playable1-GGUF-Q4_K_M

List all available models

lemonade list

Playable1-GGUF

This is a GGUF quantized version (q4_k_m) of Qwen/Qwen2.5-Coder-7B-Instruct fine-tuned with the 'iat-05-1' adapter.

Model Details

Base Model: Qwen/Qwen2.5-Coder-7B-Instruct
Adapter: iat-05-1
Quantization: q4_k_m
Format: GGUF

Usage

This model can be used with llama.cpp or any compatible inference engine that supports GGUF format.

# Example with llama.cpp
./llama-cli -m Playable1-q4_k_m.gguf -p "Your prompt here"

Files

Playable1-q4_k_m.gguf - Quantized model in GGUF format (q4_k_m)

Downloads last month: 48

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for playable/Playable1-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Quantized

(183)

this model