Instructions to use davidryalpug/playa-angel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davidryalpug/playa-angel with Transformers:

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

pipe = pipeline("text-generation", model="davidryalpug/playa-angel")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("davidryalpug/playa-angel", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use davidryalpug/playa-angel 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 davidryalpug/playa-angel:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf davidryalpug/playa-angel:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf davidryalpug/playa-angel:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf davidryalpug/playa-angel:Q3_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 davidryalpug/playa-angel:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf davidryalpug/playa-angel:Q3_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 davidryalpug/playa-angel:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf davidryalpug/playa-angel:Q3_K_M

Use Docker

docker model run hf.co/davidryalpug/playa-angel:Q3_K_M

LM Studio
Jan

vLLM

How to use davidryalpug/playa-angel with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "davidryalpug/playa-angel"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davidryalpug/playa-angel",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/davidryalpug/playa-angel:Q3_K_M

SGLang

How to use davidryalpug/playa-angel 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 "davidryalpug/playa-angel" \
    --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": "davidryalpug/playa-angel",
		"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 "davidryalpug/playa-angel" \
        --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": "davidryalpug/playa-angel",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use davidryalpug/playa-angel with Ollama:
```
ollama run hf.co/davidryalpug/playa-angel:Q3_K_M
```

Unsloth Studio

How to use davidryalpug/playa-angel 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 davidryalpug/playa-angel 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 davidryalpug/playa-angel to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for davidryalpug/playa-angel to start chatting

How to use davidryalpug/playa-angel with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf davidryalpug/playa-angel:Q3_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": "davidryalpug/playa-angel:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use davidryalpug/playa-angel with Docker Model Runner:
```
docker model run hf.co/davidryalpug/playa-angel:Q3_K_M
```

Lemonade

How to use davidryalpug/playa-angel with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull davidryalpug/playa-angel:Q3_K_M

Run and chat with the model

lemonade run user.playa-angel-Q3_K_M

List all available models

lemonade list

Hermes Agent

How to use davidryalpug/playa-angel with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf davidryalpug/playa-angel:Q3_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 davidryalpug/playa-angel:Q3_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use davidryalpug/playa-angel with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf davidryalpug/playa-angel:Q3_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "davidryalpug/playa-angel:Q3_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

davidryalpug commited on 4 days ago

Commit

806af31

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1 Parent(s): 89c909a

card: copy pass, plain sentences

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README.md +5 -5

README.md CHANGED Viewed

@@ -22,9 +22,9 @@ tags:
 # Playa Angel
 **The app these models power is open source:**
-[github.com/simbi-community-dev/playapal](https://github.com/simbi-community-dev/playapal)
-— an offline, on-device Burning Man guide (v0.5.0 released, signed APK on the
-[releases page](https://github.com/simbi-community-dev/playapal/releases)).
 Website: [playapal.lol](https://playapal.lol).
 Playa Angel is a family of local language models adapted for **Playa Pal**, an offline Black Rock City companion. Playa Pal supplies current events, guide passages, and camper-created packs through on-device search tools; the model is the conversational and tool-use layer, not the source of record for current field facts.
@@ -79,8 +79,8 @@ This is only a model-load smoke test. Do not treat a standalone answer as curren
 ## Training and provenance
 `angel-v44g-e4b-q4_0.gguf` (v4.4g) is a supervised fine-tune derived from
-[`google/gemma-4-E4B-it-qat-q4_0-unquantized`](https://huggingface.co/google/gemma-4-E4B-it-qat-q4_0-unquantized)
-— the quantization-aware-trained variant, so the shipped Q4_0 matches the
 base's intended precision.
 `angel-smart-q4_0.gguf` and `angel-light-q3_k_m.gguf` are quantizations of one supervised fine-tune derived from [`LiquidAI/LFM2.5-2.6B`](https://huggingface.co/LiquidAI/LFM2.5-2.6B). Every file was adapted for Playa Pal's conversational style, structured tool-use contract, and grounded-answer behavior. No reinforcement-learning phase was applied to this release.

 # Playa Angel
 **The app these models power is open source:**
+[github.com/simbi-community-dev/playapal](https://github.com/simbi-community-dev/playapal),
+an offline, on-device Burning Man guide. Signed APKs live on the
+[releases page](https://github.com/simbi-community-dev/playapal/releases).
 Website: [playapal.lol](https://playapal.lol).
 Playa Angel is a family of local language models adapted for **Playa Pal**, an offline Black Rock City companion. Playa Pal supplies current events, guide passages, and camper-created packs through on-device search tools; the model is the conversational and tool-use layer, not the source of record for current field facts.
 ## Training and provenance
 `angel-v44g-e4b-q4_0.gguf` (v4.4g) is a supervised fine-tune derived from
+[`google/gemma-4-E4B-it-qat-q4_0-unquantized`](https://huggingface.co/google/gemma-4-E4B-it-qat-q4_0-unquantized),
+the quantization-aware-trained variant, so the shipped Q4_0 matches the
 base's intended precision.
 `angel-smart-q4_0.gguf` and `angel-light-q3_k_m.gguf` are quantizations of one supervised fine-tune derived from [`LiquidAI/LFM2.5-2.6B`](https://huggingface.co/LiquidAI/LFM2.5-2.6B). Every file was adapted for Playa Pal's conversational style, structured tool-use contract, and grounded-answer behavior. No reinforcement-learning phase was applied to this release.