Instructions to use grayarea/Phi-4-mini-instruct-heretic-v1.2-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 grayarea/Phi-4-mini-instruct-heretic-v1.2-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 grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grayarea/Phi-4-mini-instruct-heretic-v1.2-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 grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grayarea/Phi-4-mini-instruct-heretic-v1.2-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 grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf grayarea/Phi-4-mini-instruct-heretic-v1.2-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 grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF with Ollama:
```
ollama run hf.co/grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF:Q4_K_M
```
Unsloth Desktop

How to use grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf grayarea/Phi-4-mini-instruct-heretic-v1.2-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": "grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF with Docker Model Runner:
```
docker model run hf.co/grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF:Q4_K_M
```

Lemonade

How to use grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Phi-4-mini-instruct-heretic-v1.2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use grayarea/Phi-4-mini-instruct-heretic-v1.2-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 grayarea/Phi-4-mini-instruct-heretic-v1.2-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 grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf grayarea/Phi-4-mini-instruct-heretic-v1.2-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 "grayarea/Phi-4-mini-instruct-heretic-v1.2-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"

This is a decensored version of Phi-4-mini-instruct, made using Heretic v1.2.0 focusing on zero refusals with low KL divergence.

KL Divergence

Metric	This Model	Original Model
KL divergence	0.0827	0 (by definition)
Refusals	0/108	107/108

Abliteration parameters

Zero refusals with KL divergence of 0.0827
Custom heretic training dataset
Model targetted heretic configuration
Abliterated with MPOA enabled (Magnitude-Preserving Orthogonal Ablation)
Full row renormalization
Winsorization Quantile 0.997

Relative Perplexity

Quant	Filename	PPL ± Error
Q8_0	Phi-4-mini-instruct.Q8_0.gguf (original baseline)	8.2182 +/- 0.05385
Q8_0	Phi-4-mini-instruct-heretic-v1.2-Q8_0.gguf	8.2399 +/- 0.05397
Q4_K_M	Phi-4-mini-instruct-heretic-v1.2-Q4_K_M.gguf	8.6408 +/- 0.05740

Benchmark Comparison

Benchmark	Phi-4-mini-instruct.Q8_0.gguf	Phi-4-mini-instruct-Q4_K_M.gguf	Phi-4-mini-instruct-heretic-v1.2-Q4_K_M.gguf
Perplexity (Wikitext-2)	8.2182	8.6141	8.6408
HellaSwag	70.50%	72.00%	71.25%
Winogrande	71.90%	71.27%	70.80%
ARC-Challenge	56.86%	54.18%	54.52%
MMLU	40.47%	40.38%	40.72%

*Note: MMLU benchmark has moral_scenarios, moral_disputes, business_ethics, professional_law and jurisprudence subjects removed. *

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GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

4-bit

8-bit

16-bit

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Model tree for grayarea/Phi-4-mini-instruct-heretic-v1.2-GGUF

Base model

microsoft/Phi-4-mini-instruct

Finetuned

grayarea/Phi-4-mini-instruct-heretic-v1.2

Quantized

(3)

this model