Instructions to use WPAI-INC/WPAIGPT-fse-patterns-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WPAI-INC/WPAIGPT-fse-patterns-1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="WPAI-INC/WPAIGPT-fse-patterns-1",
	filename="Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use WPAI-INC/WPAIGPT-fse-patterns-1 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 WPAI-INC/WPAIGPT-fse-patterns-1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf WPAI-INC/WPAIGPT-fse-patterns-1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WPAI-INC/WPAIGPT-fse-patterns-1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf WPAI-INC/WPAIGPT-fse-patterns-1: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 WPAI-INC/WPAIGPT-fse-patterns-1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf WPAI-INC/WPAIGPT-fse-patterns-1: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 WPAI-INC/WPAIGPT-fse-patterns-1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WPAI-INC/WPAIGPT-fse-patterns-1:Q4_K_M

Use Docker

docker model run hf.co/WPAI-INC/WPAIGPT-fse-patterns-1:Q4_K_M

LM Studio
Jan

vLLM

How to use WPAI-INC/WPAIGPT-fse-patterns-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WPAI-INC/WPAIGPT-fse-patterns-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WPAI-INC/WPAIGPT-fse-patterns-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WPAI-INC/WPAIGPT-fse-patterns-1:Q4_K_M

Ollama
How to use WPAI-INC/WPAIGPT-fse-patterns-1 with Ollama:
```
ollama run hf.co/WPAI-INC/WPAIGPT-fse-patterns-1:Q4_K_M
```

Unsloth Studio

How to use WPAI-INC/WPAIGPT-fse-patterns-1 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 WPAI-INC/WPAIGPT-fse-patterns-1 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 WPAI-INC/WPAIGPT-fse-patterns-1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for WPAI-INC/WPAIGPT-fse-patterns-1 to start chatting

How to use WPAI-INC/WPAIGPT-fse-patterns-1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WPAI-INC/WPAIGPT-fse-patterns-1: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": "WPAI-INC/WPAIGPT-fse-patterns-1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use WPAI-INC/WPAIGPT-fse-patterns-1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WPAI-INC/WPAIGPT-fse-patterns-1:Q4_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 WPAI-INC/WPAIGPT-fse-patterns-1:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use WPAI-INC/WPAIGPT-fse-patterns-1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WPAI-INC/WPAIGPT-fse-patterns-1:Q4_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 "WPAI-INC/WPAIGPT-fse-patterns-1:Q4_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"

Docker Model Runner
How to use WPAI-INC/WPAIGPT-fse-patterns-1 with Docker Model Runner:
```
docker model run hf.co/WPAI-INC/WPAIGPT-fse-patterns-1:Q4_K_M
```

Lemonade

How to use WPAI-INC/WPAIGPT-fse-patterns-1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WPAI-INC/WPAIGPT-fse-patterns-1:Q4_K_M

Run and chat with the model

lemonade run user.WPAIGPT-fse-patterns-1-Q4_K_M

List all available models

lemonade list

WPAIGPT-fse-patterns-1

WPAIGPT-fse-patterns-1 is a fine-tuned version of the Qwen 2.5 32B Instruct model, specialized for generating structured WordPress Gutenberg Patterns.

Model Details

Developed by: WPAI Inc, James LePage
Model type: Language model for generating WordPress block patterns
Language(s): Multilingual, with primary focus on English
License: Apache 2.0
Finetuned from model: Qwen 2.5 32B Instruct

Uses

Generating WordPress Gutenberg block patterns
Assisting WordPress developers and content creators
Providing inspiration for site layouts and designs
Automating the creation of structured WordPress content

Training Data

The model was fine-tuned on a dataset derived from the WordPress.org Pattern Repository, containing:

System prompts for WordPress pattern generation
User requests based on pattern descriptions
Assistant responses with WordPress Pattern Structures

The dataset includes patterns in multiple languages and covers all available WordPress blocks as of September 2024.

Training Procedure

Fine-tuning was performed on A100 GPUs (80GB each) using a conversational format. The process included:

Structuring data as system prompts, user requests, and assistant responses
Learning rate scheduling for optimal convergence
Periodic evaluation on a validation set

Evaluation Results

Formal evaluations have not been conducted. Performance is assessed through:

Manual review of generated patterns
Comparison to original patterns in the WordPress repository
Testing in real-world WordPress environments

Ethical Considerations

Users should be aware of potential biases in the training data that may affect pattern suggestions. Care should be taken to review and customize generated patterns for accessibility and inclusivity.

Limitations and Biases

Knowledge limited to patterns available in the WordPress repository as of September 2024
Potential biases towards popular block types and layouts - May not always generate valid or optimal WordPress markup

It's crucial to note that while the model generates structurally accurate patterns, full implementation within a WordPress development ecosystem requires additional tooling. Specifically, the model may reference non-existent images and URLs, necessitating a system to replace these with appropriate assets from the user's WordPress site. This functionality is being developed for integration with AgentWP (https://agentwp.com).

Out-of-Scope Use

This model should not be used for:

Generating non-WordPress related content
Creating patterns with malicious intent or that violate WordPress coding standards
Replacing human creativity and judgment in website design

For more information on using WordPress block patterns, refer to the official WordPress documentation: https://wordpress.org/documentation/article/block-pattern/

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Safetensors

Model size

33B params

Tensor type

BF16

Model tree for WPAI-INC/WPAIGPT-fse-patterns-1

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-32B-Instruct

Quantized

(150)

this model

Quantizations

3 models

WPAI-INC
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WPAIGPT-fse-patterns-1

WPAIGPT-fse-patterns-1

Model Details

Uses

Training Data

Training Procedure

Evaluation Results

Ethical Considerations

Limitations and Biases

Out-of-Scope Use

Model tree for WPAI-INC/WPAIGPT-fse-patterns-1

Dataset used to train WPAI-INC/WPAIGPT-fse-patterns-1