Instructions to use bldeaw/m1_planner_q4_k_m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use bldeaw/m1_planner_q4_k_m 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 bldeaw/m1_planner_q4_k_m:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bldeaw/m1_planner_q4_k_m:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bldeaw/m1_planner_q4_k_m:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bldeaw/m1_planner_q4_k_m: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 bldeaw/m1_planner_q4_k_m:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bldeaw/m1_planner_q4_k_m: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 bldeaw/m1_planner_q4_k_m:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bldeaw/m1_planner_q4_k_m:Q4_K_M

Use Docker

docker model run hf.co/bldeaw/m1_planner_q4_k_m:Q4_K_M

LM Studio
Jan
Ollama
How to use bldeaw/m1_planner_q4_k_m with Ollama:
```
ollama run hf.co/bldeaw/m1_planner_q4_k_m:Q4_K_M
```

Unsloth Studio

How to use bldeaw/m1_planner_q4_k_m 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 bldeaw/m1_planner_q4_k_m 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 bldeaw/m1_planner_q4_k_m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bldeaw/m1_planner_q4_k_m to start chatting

How to use bldeaw/m1_planner_q4_k_m with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bldeaw/m1_planner_q4_k_m: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": "bldeaw/m1_planner_q4_k_m:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bldeaw/m1_planner_q4_k_m with Docker Model Runner:
```
docker model run hf.co/bldeaw/m1_planner_q4_k_m:Q4_K_M
```

Lemonade

How to use bldeaw/m1_planner_q4_k_m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bldeaw/m1_planner_q4_k_m:Q4_K_M

Run and chat with the model

lemonade run user.m1_planner_q4_k_m-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bldeaw/m1_planner_q4_k_m with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bldeaw/m1_planner_q4_k_m: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 bldeaw/m1_planner_q4_k_m:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bldeaw/m1_planner_q4_k_m with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bldeaw/m1_planner_q4_k_m: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 "bldeaw/m1_planner_q4_k_m: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"

PlannerLLM

OpenAI-compatible API for bldeaw/m1_planner_q4_k_m using llama.cpp on Hugging Face Spaces.

Features

Docker Space ready
Persistent model storage
Secure secret handling
Runtime config via environment variables
Auto model download on first start
OpenAI-compatible endpoints

Required Hugging Face Setup

1. Create Docker Space

Create a new Hugging Face Space:

SDK: Docker

2. Optional: Add Persistent Storage

Settings → Storage

Recommended — prevents re-downloading the model on every restart.

Mounted path:

/data

3. Add Variables / Secrets

Settings → Variables and Secrets

Variables

MODEL_REPO_ID=bldeaw/m1_planner_q4_k_m
MODEL_FILE=m1_planner_q4_k_m.gguf
MODEL_REVISION=main
CTX_SIZE=2048
THREADS=2
N_GPU_LAYERS=0
MODEL_DIR=/data/models
PORT=7860

Secret (only if private repo)

HF_TOKEN=hf_xxxxx

Optional checksum

MODEL_SHA256=xxxxxxxx

Repository Files

Dockerfile
start.sh
.dockerignore
README.md

Deploy

git clone https://huggingface.co/spaces/bldeaw/planner-llm
cp Dockerfile start.sh .dockerignore README.md planner-llm/
cd planner-llm
git add .
git commit -m "production deploy"
git push origin main

API Usage

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://bldeaw-planner-llm.hf.space/v1",
    api_key="dummy"
)

resp = client.chat.completions.create(
    model="local-model",
    messages=[
        {"role": "user", "content": "Hello"}
    ]
)

print(resp.choices[0].message.content)

curl

curl https://bldeaw-planner-llm.hf.space/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "local-model",
    "messages": [
      {"role": "user", "content": "Hello"}
    ]
  }'

Endpoints

Endpoint	Method
/v1/chat/completions	POST
/v1/completions	POST
/v1/models	GET
/	GET

Troubleshooting

503 Service Unavailable

Usually:

Space building
Model downloading (~986 MB, takes a few minutes)
Crash during startup

Check the Logs tab.

checksum mismatch

Wrong file or corrupted download. Delete the cached file and restart.

Slow startup

Large model downloading on first run. Add persistent storage so it only downloads once.

Security

Store tokens only in Secrets
Use a private model repo + HF_TOKEN if needed
Never commit .env
Never commit .gguf into the Space repo

Downloads last month: 6

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

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