Instructions to use evalengine/decision-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 evalengine/decision-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 evalengine/decision-4b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf evalengine/decision-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 evalengine/decision-4b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf evalengine/decision-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 evalengine/decision-4b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf evalengine/decision-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 evalengine/decision-4b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf evalengine/decision-4b-gguf:Q4_K_M

Use Docker

docker model run hf.co/evalengine/decision-4b-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use evalengine/decision-4b-gguf with Ollama:
```
ollama run hf.co/evalengine/decision-4b-gguf:Q4_K_M
```
Unsloth Desktop

How to use evalengine/decision-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 evalengine/decision-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": "evalengine/decision-4b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use evalengine/decision-4b-gguf with Docker Model Runner:
```
docker model run hf.co/evalengine/decision-4b-gguf:Q4_K_M
```

Lemonade

How to use evalengine/decision-4b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull evalengine/decision-4b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.decision-4b-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use evalengine/decision-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 evalengine/decision-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 evalengine/decision-4b-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use evalengine/decision-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 evalengine/decision-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 "evalengine/decision-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"

decision-4b-gguf / NOTICE

johnsonchromia

Add Decision-0.8B comparison and shorten license text

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	Decision-4B — experimental decision adapter
	Copyright 2026 Eval Engine contributors

	New Eval Engine code and adapter modifications are licensed under Apache-2.0
	to the extent of the contributors' rights. This notice is informational and
	does not replace any source's license or grant rights in third-party data.

	Base model: Qwen/Qwen3.5-4B, Qwen Team / Alibaba.
	Revision: 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a.
	License: Apache License 2.0, included in licenses/QWEN-LICENSE.
	https://huggingface.co/Qwen/Qwen3.5-4B
	This package contains Qwen base weights modified by the Decision LoRA, merged and converted to GGUF.

	Training recipe reference: togethercomputer/tev1.
	Revision: 1dde7782382c9f49d627153759b8d1deab426ce0.
	Copyright (c) 2026 open-jev contributors.
	MIT license is retained in licenses/TEV-LICENSE.
	https://github.com/togethercomputer/tev1
	Eval Engine wrote the local trainer, data additions, evaluation and packaging.
	The adapter's copied metadata names the public base and exact base revision;
	the source run and tensor bytes are preserved.

	Dataset acknowledgments (actual selected sources/counts are in the model card):
	- MultiNLI: NYU / MultiNLI authors; mixed-source corpus terms.
	- BoolQ: Google Research / BoolQ authors; CC-BY-SA-3.0 dataset metadata.
	- Banking77: PolyAI / Banking77 authors; CC-BY-4.0 dataset metadata.
	- CLINC150: CLINC / oos-eval authors; CC-BY-3.0.
	- PAWS-Wiki: Google LLC / PAWS authors; custom any-purpose dataset license;
	underlying Wikipedia provenance remains relevant.
	- VitaminC: Tal Schuster and coauthors; Wikipedia and FEVER contributors;
	applicable article terms with CC-BY-SA-3.0 fallback in the author data license.
	- HelpSteer2: NVIDIA and contributors; CC-BY-4.0.
	- GoEmotions: Google Research and contributors; Apache-2.0 dataset metadata;
	underlying Reddit-text provenance remains relevant.
	- Synthetic policies, routing, and fictional research taxonomy: contributors
	to togethercomputer/tev1, under the repository's MIT notice.

	AG News and SST-5, if present in a selected baseline, have unresolved source
	license metadata in the Tev provenance record. They are excluded from the
	expanded model's training but remain in some evaluation sets. The package does
	not contain corpus text from any of these sources. See SOURCES.json and
	SOURCE_REVIEW.md for exact links, revisions and remaining review questions.