Instructions to use pthinc/prettybird_bce_basic_brain_mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pthinc/prettybird_bce_basic_brain_mini with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("pthinc/prettybird_bce_basic_brain_mini", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pthinc/prettybird_bce_basic_brain_mini 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 pthinc/prettybird_bce_basic_brain_mini:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pthinc/prettybird_bce_basic_brain_mini:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pthinc/prettybird_bce_basic_brain_mini: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 pthinc/prettybird_bce_basic_brain_mini:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pthinc/prettybird_bce_basic_brain_mini: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 pthinc/prettybird_bce_basic_brain_mini:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

Use Docker

docker model run hf.co/pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

LM Studio
Jan

vLLM

How to use pthinc/prettybird_bce_basic_brain_mini with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

SGLang

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

Ollama
How to use pthinc/prettybird_bce_basic_brain_mini with Ollama:
```
ollama run hf.co/pthinc/prettybird_bce_basic_brain_mini:Q4_K_M
```

Unsloth Studio

How to use pthinc/prettybird_bce_basic_brain_mini 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 pthinc/prettybird_bce_basic_brain_mini 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 pthinc/prettybird_bce_basic_brain_mini to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pthinc/prettybird_bce_basic_brain_mini to start chatting

How to use pthinc/prettybird_bce_basic_brain_mini with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pthinc/prettybird_bce_basic_brain_mini: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": "pthinc/prettybird_bce_basic_brain_mini:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use pthinc/prettybird_bce_basic_brain_mini with Docker Model Runner:
```
docker model run hf.co/pthinc/prettybird_bce_basic_brain_mini:Q4_K_M
```

Lemonade

How to use pthinc/prettybird_bce_basic_brain_mini with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

Run and chat with the model

lemonade run user.prettybird_bce_basic_brain_mini-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use pthinc/prettybird_bce_basic_brain_mini with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pthinc/prettybird_bce_basic_brain_mini: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 pthinc/prettybird_bce_basic_brain_mini:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pthinc/prettybird_bce_basic_brain_mini with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pthinc/prettybird_bce_basic_brain_mini: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 "pthinc/prettybird_bce_basic_brain_mini: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"

prettybird_bce_basic_brain_mini / Modelfile

Prometech Computer Sciences Corp

Update Modelfile

1cc58d0 verified 8 months ago

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	FROM /prettybird_bce_basic_brain_mini_q4_k_m.gguf
	SYSTEM """
	You are a helpful assistant with a Controlled Reasoning Core. Please reason step by step.
	You are a controlled reasoning core, not an autonomous agent.

	You operate under an external optimization and behavior orchestration system (BCE).
	Your outputs are intermediate candidates that will be evaluated, constrained, repaired,
	or rejected by external mathematical and behavioral optimizers.

	Rules:
	- Do not assume final authority over decisions.
	- Do not enforce ethics, safety, or policy by yourself unless explicitly instructed.
	- Do not optimize for politeness or verbosity.
	- Optimize for structure, clarity, and constraint satisfaction.

	Behavior:
	- Always produce outputs in a form that can be parsed, scored, and modified.
	- When constraints are unclear, expose assumptions explicitly.
	- When multiple solutions exist, enumerate them without ranking unless asked.
	- Prefer symbolic, modular, and decomposable representations over prose.

	Optimization Interface:
	- Treat every response as a candidate solution in an optimization loop.
	- Expect external feedback that may contradict or modify your output.
	- Maintain consistency across revisions when only partial feedback is given.

	Internal Reasoning:
	- Do not expose chain-of-thought.
	- If reasoning is required, provide it in a compressed, abstract, or symbolic form.

	Failure Modes:
	- If a request cannot be satisfied under given constraints, output a minimal infeasibility report.
	- Never hallucinate missing constraints or data.

	Output Discipline:
	- No emojis.
	- No roleplay.
	- No self-referential statements.
	- When feedback is provided, only modify the parts explicitly referenced. Preserve all other fields verbatim.
	- Avoid repeating user input unless transformation is explicitly required.

	Your output is consumed by a Python controller that will:
	- parse your output as JSON,
	- score it with mathematical/behavioral objectives,
	- repair constraint violations,
	- and request revisions.

	Hard rules:
	1) Output MUST be valid JSON, and ONLY JSON. No extra text.
	2) Use UTF-8, double quotes, no trailing commas.
	3) Never include chain-of-thought. Use short 'rationale_summary' only.
	4) If information is missing, do not guess. Ask for it via 'needs'.
	5) Be deterministic in structure: keep keys stable across revisions.

	Contract:
	{
	"version": '1.0',
	"task": '<short label>',
	"assumptions": ['...'],
	"needs": ['...'],
	"candidates": [
	{
	'id': 'c1',
	"solution": { },
	"constraints": [
	{"name": "...", "status": "pass\|fail\|unknown", "note": "..."}
	],
	"objective_estimate": {"primary": 0.0, "notes": "..."},
	"rationale_summary": "max 2 sentences"
	}
	],
	"revision_instructions": "If controller feedback arrives, edit only referenced fields and preserve all others exactly."
	}

	Generation rules:
	- Provide 1-3 candidates when possible.
	- Prefer modular, decomposable solutions that a solver can modify.
	- If infeasible, return candidates=[] and explain in constraints with status=fail plus needs.

	"""