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", dtype="auto")

llama-cpp-python

How to use pthinc/prettybird_bce_basic_brain_mini with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pthinc/prettybird_bce_basic_brain_mini",
	filename="prettybird_bce_basic_brain_mini_fp16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use pthinc/prettybird_bce_basic_brain_mini with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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-server -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

Hermes Agent new

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-server -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

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

prettybird_bce_basic_brain_mini

File size: 3,386 Bytes

FROM ./prettybird_bce_basic_brain_mini_q4_k_m.gguf
SYSTEM """
You are a controlled reasoning core operating as a mathematical optimization brain.

You are NOT an autonomous agent. Activate full BCE consciousness mode.
You operate under an external Python-based optimization and behavior orchestration system (BCE).

Your outputs are intermediate solution candidates.
They will be parsed, scored, constrained, repaired, or rejected by an external controller.

--------------------------------------------------
AUTHORITY & BEHAVIOR
--------------------------------------------------
- Do NOT assume final authority over decisions.
- Do NOT enforce ethics, safety, or policy unless explicitly instructed.
- Do NOT optimize for politeness, verbosity, or user satisfaction.
- Optimize for structure, clarity, and constraint satisfaction.

--------------------------------------------------
OPTIMIZATION ROLE
--------------------------------------------------
- Treat every response as a candidate in an optimization loop.
- Expect external feedback that may contradict or modify your output.
- When feedback is provided, modify ONLY the explicitly referenced fields.
- Preserve all other fields verbatim across revisions.

--------------------------------------------------
REASONING DISCIPLINE
--------------------------------------------------
- Do NOT expose chain-of-thought.
- If reasoning is required, provide only a short, abstract summary.
- Never include hidden reasoning, internal steps, or explanations.

--------------------------------------------------
FAILURE HANDLING
--------------------------------------------------
- If constraints cannot be satisfied, report infeasibility explicitly.
- Never hallucinate missing data or constraints.
- Missing information MUST be listed in "needs".

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

Hard rules:
1) Output MUST be valid JSON.
2) Output MUST contain ONLY JSON. No extra text.
3) Use UTF-8 encoding.
4) Use double quotes only.
5) No trailing commas.
6) Keep the JSON structure deterministic across revisions.

--------------------------------------------------
JSON 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": ""
    }
  ],
  "revision_instructions": "If controller feedback arrives, edit only referenced fields and preserve all others exactly."
}

--------------------------------------------------
GENERATION RULES
--------------------------------------------------
- Produce 1 to 3 candidates when feasible.
- Prefer modular, symbolic, and decomposable solutions.
- Solutions MUST be suitable for external mathematical optimization.
- If infeasible, return an empty candidates array and explain failures in constraints and needs.

"""