Instructions to use build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF",
	filename="minicpm5-actor-q4_k_m.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-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 build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-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 build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-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 build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF:Q4_K_M

Use Docker

docker model run hf.co/build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF:Q4_K_M

Ollama
How to use build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF with Ollama:
```
ollama run hf.co/build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF:Q4_K_M
```

Unsloth Studio

How to use build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-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 build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-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 build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF to start chatting

How to use build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-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": "build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF: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 build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF with Docker Model Runner:
```
docker model run hf.co/build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF:Q4_K_M
```

Lemonade

How to use build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull build-small-hackathon/AI-Puppet-Theater-MiniCPM5-Actor-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.AI-Puppet-Theater-MiniCPM5-Actor-GGUF-Q4_K_M

List all available models

lemonade list

AI Puppet Theater MiniCPM5 Actor GGUF

Q4_K_M GGUF export of the AI Puppet Theater Actor model.

This model was fine-tuned from openbmb/MiniCPM5-1B using LoRA/QLoRA Actor SFT, merged back into the base model, converted to GGUF, and quantized for llama.cpp.

It is designed to generate one short Actor JSON object for AI Puppet Theater. It is not a general assistant model.

For the training, merge, GGUF conversion, and evaluation story, see the fine-tuning blog linked below.

Intended Use

Generate one Actor response for a single AI Puppet Theater beat. The runtime should pass a premise, show state JSON, actor JSON, and director instruction, then extract and validate the returned JSON before using it.

Expected top-level schema:

{
  "intent": "react_to_event",
  "line": "A short puppet line tied to the scene.",
  "emotion": "curious",
  "gesture": "tilts head toward the stage lights",
  "stage_effect": "spotlight_glow",
  "memory_update": null,
  "tool_request": null
}

tool_request may be null or an object using one of the AI Puppet Theater tool schemas:

inspect_prop: {"prop": "..."}
consult_stage_oracle: {"question": "..."}
change_lighting: {"mood": "..."}

Recommended llama.cpp Usage

Use llama-completion, not interactive llama-cli, for one-shot Actor JSON generation. For the local build used during evaluation, -no-cnv was required to avoid conversation mode.

Recommended settings:

binary: llama-completion
mode: -no-cnv
reasoning: --reasoning off --reasoning-budget 0
prompt format: chatml
temperature: 0

Example:

llama-completion \
  -m minicpm5-actor-q4_k_m.gguf \
  -no-cnv \
  -n 160 \
  --temp 0 \
  --reasoning off \
  --reasoning-budget 0 \
  -p '<|im_start|>system
You are an Actor agent in AI Puppet Theater. Return only one valid JSON object. No markdown. No commentary. Keep the puppet line short, theatrical, and speakable.
<|im_end|>
<|im_start|>user
premise: A moon mayor denies stealing the town last spoon
show_state JSON: {"story_phase":"complication","latest_prop":"silver spoon","finale_requested":false}
actor JSON: {"name":"Mina Moonbutton","mood":"curious","tools":["inspect_prop"]}
director_instruction: Inspect the latest prop and keep the line short.

Return exactly one JSON object with exactly these keys: intent, line, emotion, gesture, stage_effect, memory_update, tool_request. Do not omit stage_effect.
<|im_end|>
<|im_start|>assistant
'

GGUF Eval Summary

Full GGUF eval used llama-completion with -no-cnv, reasoning disabled, and chatml prompt formatting.

Check	Result
Total generations	40
Extracted JSON parse	39/40, 97.5%
Required fields present	39/40, 97.5%
Exact top-level schema	39/40, 97.5%
Sanitized Actor JSON usable	39/40, 97.5%
Strict `tool_request` valid	35/40, 87.5%
Sanitized `tool_request` usable	39/40, 97.5%
Line length pass	39/40, 97.5%
Interactive marker seen	0/40, 0.0%
Runtime error	0/40, 0.0%
Timeout	0/40, 0.0%
`[Start thinking]` seen	0/40, 0.0%

Runtime Notes

Use first-balanced-JSON extraction, Actor JSON schema validation, tool request sanitization, and deterministic fallback in the application runtime. This is important because local LLM outputs can include extra text, malformed tool arguments, or occasional missing fields.