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"

Playa Angel

The app these models power is open source: github.com/simbi-community-dev/playapal, an offline, on-device Burning Man guide. Signed APKs live on the releases page. Website: 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.

The repository contains three GGUF files from two fine-tune lineages:

File	App tier	Base model	Quantization	Size	SHA-256
`angel-v44g-e4b-q4_0.gguf`	Playa Angel Max	Gemma 4 E4B	Q4_0	5,154,940,832 bytes	`5a6b4d85cb8da811f04496498c3302772339e20914adebff4d4718b348f73c53`
`angel-smart-q4_0.gguf`	Playa Angel	LFM2.5-2.6B	Q4_0	1,593,894,208 bytes	`653c7ee3e7e95468516574350b0cfacbadb4609bf56d8789e452f9b1297e0bb4`
`angel-light-q3_k_m.gguf`	Playa Angel Light	LFM2.5-2.6B	Q3_K_M	1,366,844,736 bytes	`f5fb59a0db7039f4c485f82f6d448e2126d3923b5bee1ac411b5aea67c85de31`

The app recommends the largest tier the phone's measured memory clears and keeps every tier explicitly selectable. The Max file needs a recent phone with generous memory; the two LFM files serve mid-range and lower-memory phones.

Intended use

These weights are intended for local inference inside Playa Pal, where the app provides:

an application-specific system prompt and tool schema;
on-device event and guide retrieval;
structured event cards and cited source passages;
local conversation history and camper-selected packs.

The model can also be loaded by a compatible GGUF runtime for basic experimentation, but standalone use does not reproduce Playa Pal's grounding, tools, safety copy, or current event data.

Use in Playa Pal

Playa Pal downloads an immutable, commit-pinned file, verifies its complete SHA-256 digest, and only then installs it. In the app, open the Angel model chooser and select Playa Angel or Playa Angel Light.

Download models and any offline speech data over a reliable connection before travel. Core chat, saved events, guides, and local camp data run on the phone after setup.

Standalone load check

With a recent llama.cpp build:

llama-cli -m angel-smart-q4_0.gguf -cnv \
  -p "Explain what information you can and cannot verify without Playa Pal's local tools."

This is only a model-load smoke test. Do not treat a standalone answer as current event, navigation, medical, legal, or emergency guidance.

Limitations and safety

A small local model can misunderstand a request, skip a needed tool, contradict retrieved evidence, or invent plausible details.
The weights do not contain a live event database. Current events and guide facts must come from the app's enabled local packs.
Quantization can change output quality. The Light tier trades additional quality margin for lower storage and memory use.
Device support is not universal; available RAM, free storage, operating-system memory pressure, and runtime support all matter.
Verify safety-critical information against authoritative sources. In an emergency, contact on-playa responders rather than relying on generated text.
Do not use the model as the sole basis for medical, legal, financial, or emergency decisions.

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, 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. 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.

The exact upstream revision, fine-tuning code revision, training-data manifest, merge tool, and GGUF conversion/quantization versions should be recorded here when confirmed; this card does not invent values that are absent from the public release record.

Evaluation

The two files were selected through claim-level evaluation for the Playa Pal workflow. A reproducible public evaluation report is not yet included in this repository. Until one is published, treat release-selection claims as project-reported rather than independently reproducible.

License

Licenses apply per file, inherited from each file's base model:

angel-smart-q4_0.gguf and angel-light-q3_k_m.gguf are distributed under the LFM Open License v1.0 (LICENSE); it includes conditions tied to commercial use and annual revenue.
angel-v44g-e4b-q4_0.gguf is a Gemma derivative provided under and subject to the Gemma Terms of Use.

NOTICE describes the derivative relationships.

Integrity

SHA256SUMS contains the authoritative full-file digests. Verify after download:

sha256sum -c SHA256SUMS

The application catalog pins both file sizes and full digests in addition to using immutable revision URLs.

Downloads last month: 120

GGUF

Model size

3B params

Architecture

lfm2

Hardware compatibility

3-bit

4-bit

Model tree for davidryalpug/playa-angel

Base model

LiquidAI/LFM2.5-2.6B-Base

Finetuned

LiquidAI/LFM2.5-2.6B

Quantized

(63)

this model