Instructions to use Mapika/decider-4b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Mapika/decider-4b-GGUF 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 Mapika/decider-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Mapika/decider-4b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Mapika/decider-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Mapika/decider-4b-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 Mapika/decider-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Mapika/decider-4b-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 Mapika/decider-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mapika/decider-4b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Mapika/decider-4b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Mapika/decider-4b-GGUF with Ollama:
```
ollama run hf.co/Mapika/decider-4b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Mapika/decider-4b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mapika/decider-4b-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Mapika/decider-4b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Mapika/decider-4b-GGUF with Docker Model Runner:
```
docker model run hf.co/Mapika/decider-4b-GGUF:Q4_K_M
```

Lemonade

How to use Mapika/decider-4b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mapika/decider-4b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.decider-4b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Mapika/decider-4b-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mapika/decider-4b-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 Mapika/decider-4b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Mapika/decider-4b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Mapika/decider-4b-GGUF: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 "Mapika/decider-4b-GGUF: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"

decider-4b-GGUF / decider_config.json

Mapika

decider-4b v2.1 GGUF: Q4_K_M, Q8_0, BF16 + decide_gguf.py

26c404e verified 9 days ago

Raw History Blame

1.38 kB

	{
	"temperature": 1.099,
	"temperature_by_type": {
	"choice": 1.11,
	"noul": 1.56,
	"score": 1.287
	},
	"neutralize_none": false,
	"version": "4b-v2.1",
	"base": "Mapika/decider-4b v1 + LoRA (merged); v1 is Qwen/Qwen3.5-4B-Base + one supervised pass over mixture v2",
	"layout": "plain",
	"max_options": 255,
	"max_state_tokens": 32768,
	"schema_first": false,
	"schema_first_trained": false,
	"isolated_levels": true,
	"release_date": "2026-09-24",
	"requires": "decider-ai>=1.4.0 for temperature_by_type; older versions serve every answer at temperature",
	"stage": "decider-4b v1 + LoRA rank 64 (alpha 128) on attention and MLP, LR 1e-4, 2 epochs (1,518 steps of 65,536 tokens) over v2's 29,325-row mix in the plain state-first layout (generated decision families with code-computed answers, Qwen3.6-27B-written document questions kept when two independent answers agreed, human-labelled public sets, replay of v1's mixture v2), with the replay rows trained toward v1's own answer distribution (KL to v1) instead of their labels, merged into the bf16 weights; no RL stage; temperature fitted by NLL on 61 in-task regression tasks (the 67 in-task tasks without banking77, clinc_oos, mmlu, arc, winogrande, hellaswag); temperature_by_type fitted with decider.calibrate.fit_by_type on the same regression rows plus our own validation rows (choice, noul and score answers)"
	}