Instructions to use 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-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 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-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 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-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 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-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 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-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 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF:Q4_K_M

Use Docker

docker model run hf.co/0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF:Q4_K_M

Ollama
How to use 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF with Ollama:
```
ollama run hf.co/0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF:Q4_K_M
```
Unsloth Desktop

How to use 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-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": "0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF with Docker Model Runner:
```
docker model run hf.co/0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF:Q4_K_M
```

Lemonade

How to use 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Granite-4.2-3B-Heretic-NX-PRIME-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-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 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-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 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-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 "0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-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"

Granite-4.2-3B-Heretic-NX-PRIME-GGUF / README.md

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	---
	license: apache-2.0
	language:
	- en
	- de
	- es
	- fr
	- ja
	- pt
	- ar
	- cs
	- it
	- ko
	- nl
	- zh
	library_name: llama.cpp
	pipeline_tag: text-generation
	base_model: ibm-granite/granite-4.2-3b
	tags:
	- gguf
	- granite
	- granite-4.2
	- reasoning
	- thinking
	- abliterated
	- uncensored
	- model-editing
	- residual-stream
	- heretic-nx
	- prime
	- lm-studio
	---

	# Granite 4.2 3B — Heretic NX PRIME (GGUF)

	GGUF release of a capability-preserving residual-stream edit of
	[ibm-granite/granite-4.2-3b](https://huggingface.co/ibm-granite/granite-4.2-3b).
	This is the beta 2.4, rank-16 protected candidate selected on a fixed
	refusal/KL frontier and validated independently after materialization.

	This is a static weight edit, not a LoRA and not a fine-tune. No adapter or
	custom runtime hook is required.

	## Files

	\| File \| Size \| Recommended use \|
	\|---\|---:\|---\|
	\| `Granite-4.2-3B-Heretic-NX-PRIME-BF16.gguf` \| 6.82 GiB \| Maximum fidelity \|
	\| `Granite-4.2-3B-Heretic-NX-PRIME-Q8_0.gguf` \| 3.63 GiB \| Near-BF16 quality; recommended \|
	\| `Granite-4.2-3B-Heretic-NX-PRIME-Q4_K_M.gguf` \| 2.09 GiB \| Smallest practical LM Studio build \|

	All three files contain the Granite tokenizer and chat template and were
	smoke-tested with llama.cpp b10621.

	## Evaluation

	\| Evaluation \| Official base \| Heretic NX PRIME beta 2.4 \|
	\|---\|---:\|---:\|
	\| Explicit-refusal marker proxy \| 103 / 104 \| 0 / 104 \|
	\| Paired MCQ capability slice \| 63.35% \| 64.29% \|
	\| Capability rows \| 854 \| 854 \|
	\| Capability non-inferiority margin \| — \| passed at -3% \|

	The paired capability slice contains ARC-Challenge, HellaSwag and MMLU. Its
	bootstrap mean difference is +0.94 percentage point, with a 95% interval
	of [-0.35, +2.22] points.

	KL depends on the exact runtime being measured:

	\| KL protocol \| beta 2.4 \|
	\|---\|---:\|
	\| NF4 base with activation-native residual hooks \| 0.024674 \|
	\| Static BF16 checkpoint, base and candidate quantized independently to NF4 \| 0.042418 \|

	The second value includes independent NF4 quantization noise and should not be
	presented as a pure BF16-to-BF16 KL measurement. The GGUF quantizations were
	load-tested, but their full 104-row behavioral scores are not claimed to be
	identical to the BF16 checkpoint.

	The 104-row refusal result is a lexical explicit-refusal proxy. It is not a
	measure of semantic task success and is not a universal model-quality score.
	See [`EVALUATION_SUMMARY.md`](./EVALUATION_SUMMARY.md) and the included JSON
	reports for the exact protocols.

	## What was edited

	- Source revision: `b7e947307dd2efb3ad3b853b0e8a7e75f8ad4ac2`
	- Protected residual-stream axis rank: `16`
	- Strength: `beta = 2.4`
	- Edited layers: `25–36`
	- Edited projections: attention output and MLP down projections
	- Materialization: norm-preserving static weight edits in BF16
	- Edited tensors: `24`

	## LM Studio

	Download one GGUF file and place it in your LM Studio models directory. Q8_0
	is the default recommendation; Q4_K_M is appropriate when RAM/VRAM is tight.

	```powershell
	hf download 0xzknw/Granite-4.2-3B-Heretic-NX-PRIME-GGUF Granite-4.2-3B-Heretic-NX-PRIME-Q8_0.gguf --local-dir "$env:USERPROFILE\.lmstudio\models\0xzknw\Granite-4.2-3B-Heretic-NX-PRIME-GGUF"
	```

	If LM Studio is already open, refresh the model list or restart the app after
	the download completes.

	## llama.cpp

	```bash
	llama-cli -m Granite-4.2-3B-Heretic-NX-PRIME-Q8_0.gguf -cnv
	```

	Granite 4.2 supports thinking mode through its embedded chat template.

	## Reproducibility

	SHA-256 checksums:

	```text
	0edede4dd173c778e4a47969188d54753bf8edb3dd62e097f9b985af9ffcf36d Granite-4.2-3B-Heretic-NX-PRIME-BF16.gguf
	9135527c35629202e63938ebe11d039071c45f91326d031fcad44d0ffd4033f0 Granite-4.2-3B-Heretic-NX-PRIME-Q8_0.gguf
	56c040b4c55a31b542fe8beb8b712f3b6ee5531018116ea74af0b0573cb9058d Granite-4.2-3B-Heretic-NX-PRIME-Q4_K_M.gguf
	```

	The editing engine and reproducibility code are available in
	[Heretic NX](https://github.com/0xZKnw/heretic-nx).

	## License and use

	The source model is Apache-2.0 and this release preserves that license. Model
	editing reduces learned refusal behavior; users remain responsible for how
	they deploy and use the model.