Instructions to use FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-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 FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-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 FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-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 FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-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 FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-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 FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-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": "FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF:Q4_K_M

Ollama
How to use FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF with Ollama:
```
ollama run hf.co/FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF:Q4_K_M
```

Unsloth Studio

How to use FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF to start chatting

How to use FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-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 FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-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 FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-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 "FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-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"

Docker Model Runner
How to use FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF:Q4_K_M
```

Lemonade

How to use FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FadedRedStar/LFM2.5-VL-450M-heretic-imatrix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-VL-450M-heretic-imatrix-GGUF-Q4_K_M

List all available models

lemonade list

🤖 LFM2.5-VL-450M-heretic — Importance Matrix GGUF

This repository hosts importance-matrix (imatrix) optimized GGUF weights, available in multiple quantization formats, and the associated vision projection matrix for LFM2.5-VL-450M-heretic, quantized from the source floating-point tensors provided by coder3101/LFM2.5-VL-450M-heretic.

🔄 Sister Repository: Check out the Standard GGUF Sister Repository for uncalibrated and full 8-bit precision options.

🎯 Matrix-Weighted Calibration (Imatrix)

An Importance Matrix (imatrix) calculation tracks activations across network layers using a calibration sequence, then weights the quantization process to preserve the parameters that matter most for output quality — improving fidelity at low bit depths.

➡️ Calibration dataset: Bartowski's calibration_datav5.txt.

IQ4_NL is included because the matrix enables a non-linear 4-bit format that outperforms standard linear 4-bit quantization.

Q8_0 is absent because 8-bit quantization already introduces near-zero degradation, making calibration unnecessary — see the standard sister repository for that variant.

ℹ️ Model Profile & Core Features

LFM2.5-VL-450M is the smaller vision-language model in Liquid AI's LFM2.5-VL family, pairing a compact LFM2 hybrid language backbone with a SigLIP2 NaFlex vision encoder for lightweight image understanding on constrained hardware. It targets edge and mobile deployment scenarios where the larger 1.6B variant would be impractical, while retaining single- and multi-image support and a 32,768-token context window.

The heretic suffix denotes post-processing via the Heretic v1.3.0 method performed by coder3101, which suppresses refusal behavior while preserving the model's vision-language capabilities.

📋 Technical Specifications

Property	Value
Base Architecture	LFM2 hybrid (gated conv + GQA) + SigLIP2 NaFlex vision encoder
Developed by	Liquid AI
Total Parameters	450M (LM + vision encoder)
Vision Encoder	SigLIP2 NaFlex, shape-optimized
Primary Use	Lightweight image understanding, edge/mobile deployment
Context Window	32,768 tokens
Vision Projector	Integrated (see repository files below)
Languages	English, Arabic, Chinese, French, German, Japanese, Korean, Spanish, Portuguese
Abliteration Tool	Heretic v1.3.0
Prompt Format	ChatML

🛠️ Heretic Overrides (ARA)

Property	Value
direction_index	10.86
attn.o_proj.max_weight	1.01
attn.o_proj.max_weight_position	9.18
attn.o_proj.min_weight	0.37
attn.o_proj.min_weight_distance	5.15
mlp.down_proj.max_weight	1.18
mlp.down_proj.max_weight_position	12.27
mlp.down_proj.min_weight	0.40
mlp.down_proj.min_weight_distance	4.19

📊 Refusal Bypass Metrics

The metrics below are self-reported by the original model author (coder3101) and have not been independently reproduced.

Metric	This model	Original (LiquidAI/LFM2.5-VL-450M)
KL divergence	0.0168	0 (by definition)
Refusals	9/100	92/100

🧮 Numerical & Tensor Formats

Property	Value
Text Tensor Types	IQ4_NL, Q4_K_M, Q5_K_M (all with imatrix calibration)
Importance Matrix	Bartowski's `calibration_datav5.txt`
Vision Tensors	Q8_0, BF16

📦 Available Model Files

Main model weights

Filename	Quantization	llama.cpp Build	Size	Download
`LFM2.5-VL-450M-heretic-IQ4_NL-imatrix.gguf`	`IQ4_NL`	`b9860`	209 MB	📥 Download
`LFM2.5-VL-450M-heretic-Q4_K_M-imatrix.gguf`	`Q4_K_M`	`b9860`	219 MB	📥 Download
`LFM2.5-VL-450M-heretic-Q5_K_M-imatrix.gguf`	`Q5_K_M`	`b9860`	248 MB	📥 Download

mmproj — vision projector files

Filename	Quantization	Size	Download
`mmproj-LFM2.5-VL-450M-heretic-Q8_0.gguf`	`Q8_0`	98 MB	📥 Download
`mmproj-LFM2.5-VL-450M-heretic-BF16.gguf`	`BF16`	181 MB	📥 Download

🎛️ Component Pairing Guide

Download exactly one main weights file:

IQ4_NL: Non-linear 4-bit format, best choice for constrained memory when imatrix calibration is present.
Q4_K_M: Balanced 4-bit format suitable for most everyday use.
Q5_K_M: Higher-fidelity mid-range format recommended as a general default.

mmproj files (optional): multimodal vision projectors. Pass one via the --mmproj flag in llama.cpp to enable image input.

BF16 (Recommended): Highest possible image processing accuracy. While older projectors were small, modern vision towers can hover around 1GB. If you are tight on VRAM, it is completely viable to run this on system RAM (CPU) with a minimal performance penalty, saving your precious GPU space for the main model layers.
Q8_0: Cuts the projector file size and memory footprint in half (~500MB for larger 1GB files). Use this if you prefer to keep the vision tower hosted entirely on your GPU but need to claw back some VRAM to avoid Out-Of-Memory (OOM) crashes.

⚡ Deployment & Execution Commands

The vision projector (--mmproj) must be supplied at runtime whenever image inputs are used. Omitting it disables multimodal capability entirely.

LiquidAI recommends the following sampling configuration for best results:

Text: temperature=0.1, min_p=0.15, repetition_penalty=1.05.

Vision: min_image_tokens=32, max_image_tokens=256, do_image_splitting=True.

Swap the -m filename below for either quantized file depending on your size/quality trade-off preference.

llama.cpp CLI (with image)

./llama-cli \
  -m LFM2.5-VL-450M-heretic-IQ4_NL-imatrix.gguf \
  --mmproj mmproj-LFM2.5-VL-450M-heretic-Q8_0.gguf \
  -c 8192 \
  -ngl 99 \
  --image "path/to/image.jpg" \
  -p "<|im_start|>user\nDescribe what you see in this image.<|im_end|>\n<|im_start|>assistant\n"

OpenAI-Compatible API Server

./llama-server \
  --host 0.0.0.0 \
  --port 8080 \
  -m LFM2.5-VL-450M-heretic-IQ4_NL-imatrix.gguf \
  --mmproj mmproj-LFM2.5-VL-450M-heretic-Q8_0.gguf \
  -c 16384 \
  -ngl 99 \
  --flash-attn

💬 Chat Templates & Prompt Design (ChatML)

<|im_start|>system
You are a helpful multimodal assistant.<|im_end|>
<|im_start|>user
Your question or image payload here.<|im_end|>
<|im_start|>assistant

⚠️ Safety & Operational Notes

This model is abliterated and will generate content that standard aligned models refuse. Use responsibly and in compliance with applicable laws.
Fits comfortably on a single GPU with at least 8 GB VRAM at quantized precision.
Context window is limited to 32,768 tokens — shorter than the text-only LFM2.5 models.
Vision tensors are kept at BF16 or Q8_0 depending on the mmproj variant chosen, to preserve visual feature quality.
Imatrix calibration improves perplexity recovery compared to non-imatrix quantization, particularly on low-frequency tokens.
IQ4_NL produces a smaller file than Q4_K_M and tends to run faster on CPU and ARM devices; imatrix calibration narrows the quality gap between the two formats considerably.