Instructions to use mindchain/decider-4b-v2-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 mindchain/decider-4b-v2-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 mindchain/decider-4b-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mindchain/decider-4b-v2-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 mindchain/decider-4b-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mindchain/decider-4b-v2-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 mindchain/decider-4b-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mindchain/decider-4b-v2-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 mindchain/decider-4b-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mindchain/decider-4b-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mindchain/decider-4b-v2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mindchain/decider-4b-v2-GGUF with Ollama:
```
ollama run hf.co/mindchain/decider-4b-v2-GGUF:Q4_K_M
```
Unsloth Desktop

How to use mindchain/decider-4b-v2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mindchain/decider-4b-v2-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": "mindchain/decider-4b-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mindchain/decider-4b-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/mindchain/decider-4b-v2-GGUF:Q4_K_M
```

Lemonade

How to use mindchain/decider-4b-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mindchain/decider-4b-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.decider-4b-v2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mindchain/decider-4b-v2-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 mindchain/decider-4b-v2-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 mindchain/decider-4b-v2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mindchain/decider-4b-v2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mindchain/decider-4b-v2-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 "mindchain/decider-4b-v2-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 v2 — Q4_K_M GGUF (revision-pinned)

decider-4b at Hub tag v2 — exactly the revision that holds JevBench v1.4.2 rank 1 (Score 64.1 · Intel 49 · Calib 75 · Speed 93 · Cost 61), self-quantized with full provenance. Community GGUFs rarely pin which revision they quantized — this one does.

Which revision, and why it matters

The upstream repo root is v2.1; v2 lives under the v2 tag. Per the upstream card, v2 wins on hard decisions and is better calibrated there (0.676 vs 0.649), while v2.1 trades some of that for sampled play. For logit-read typed decisions (SemIf-style: state + lettered options → probabilities in one forward pass, no text generation) you want v2.


Source	Mapika/decider-4b @ tag `v2` (bf16, 8.4 GB)
Quant	`Q4_K_M` via llama.cpp `convert_hf_to_gguf.py` (9575389) + `llama-quantize`
Size / SHA256	2708804544 bytes / `f7e2e510ef51d212ea9b7fb8bf27906b5f516d7939ca847428fb91f6a8acfa79`
Base	Qwen3.5-4B-Base (4.2B params, Apache-2.0)
Quantized by	mindchain (JEV stack), Kaggle CPU, pipeline in `provenance.json`

Usage — System-One decisions from logits (no generation)

llama-server -m decider-4b.v2-Q4_K_M.gguf -ngl 99 -c 2048 --port 8080

Send the SemIf minimal prompt (state + criteria + lettered options) and read the option-letter probabilities straight from the logits — one forward pass, no decoding loop. Apply the per-answer-type temperature from the upstream card for calibrated confidences. Works with any llama.cpp runtime (server, Termux/Android, ChatterUI).

Honest benchmark note (protocol separation)

JevBench v1.4.2 ranks decider-4b v2 #1 (64.1). On the independent JEV-stack gold set, a decider-4b Q4 measured 0.5975 accuracy vs 0.7635 for a fine-tuned encoder-decider (laya) — different protocols crown different winners. Measure on YOUR domain before shipping.

Benchmark: JevBench v1.4.2
JEV decision-model stack: calibration-first System-One models, gold-set gated deployments

Downloads last month: 599

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for mindchain/decider-4b-v2-GGUF

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Mapika/decider-4b

Quantized

(6)

this model