Instructions to use reaperdoesntknow/LFM2.5-2.6B-CyberSec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use reaperdoesntknow/LFM2.5-2.6B-CyberSec with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="reaperdoesntknow/LFM2.5-2.6B-CyberSec")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/LFM2.5-2.6B-CyberSec")
model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/LFM2.5-2.6B-CyberSec", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use reaperdoesntknow/LFM2.5-2.6B-CyberSec 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 reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf reaperdoesntknow/LFM2.5-2.6B-CyberSec: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 reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf reaperdoesntknow/LFM2.5-2.6B-CyberSec: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 reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M

Use Docker

docker model run hf.co/reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M

LM Studio
Jan

vLLM

How to use reaperdoesntknow/LFM2.5-2.6B-CyberSec with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reaperdoesntknow/LFM2.5-2.6B-CyberSec"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/LFM2.5-2.6B-CyberSec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M

SGLang

How to use reaperdoesntknow/LFM2.5-2.6B-CyberSec with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "reaperdoesntknow/LFM2.5-2.6B-CyberSec" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/LFM2.5-2.6B-CyberSec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "reaperdoesntknow/LFM2.5-2.6B-CyberSec" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reaperdoesntknow/LFM2.5-2.6B-CyberSec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use reaperdoesntknow/LFM2.5-2.6B-CyberSec with Ollama:
```
ollama run hf.co/reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M
```

Unsloth Studio

How to use reaperdoesntknow/LFM2.5-2.6B-CyberSec 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 reaperdoesntknow/LFM2.5-2.6B-CyberSec 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 reaperdoesntknow/LFM2.5-2.6B-CyberSec to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for reaperdoesntknow/LFM2.5-2.6B-CyberSec to start chatting

How to use reaperdoesntknow/LFM2.5-2.6B-CyberSec with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf reaperdoesntknow/LFM2.5-2.6B-CyberSec: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": "reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use reaperdoesntknow/LFM2.5-2.6B-CyberSec with Docker Model Runner:
```
docker model run hf.co/reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M
```

Lemonade

How to use reaperdoesntknow/LFM2.5-2.6B-CyberSec with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-2.6B-CyberSec-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use reaperdoesntknow/LFM2.5-2.6B-CyberSec with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf reaperdoesntknow/LFM2.5-2.6B-CyberSec: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 reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use reaperdoesntknow/LFM2.5-2.6B-CyberSec with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf reaperdoesntknow/LFM2.5-2.6B-CyberSec: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 "reaperdoesntknow/LFM2.5-2.6B-CyberSec: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"

LFM2.5-2.6B-CyberSec / README.md

reaperdoesntknow

keeper: refresh 2026-08-16

b8d0b8e verified about 16 hours ago

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	---
	license: apache-2.0
	base_model:
	- LiquidAI/LFM2.5-2.6B
	language:
	- en
	datasets:
	- Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- transformers
	- safetensors
	- gguf
	- lfm2
	- cybersecurity
	- conversational
	- unsloth
	---

	# LFM2.5-2.6B-CyberSec

	An English LFM2.5 2.6B checkpoint associated with the Trendyol Cybersecurity Instruction Tuning Dataset. This repository contains both Transformers-format model files and local GGUF exports.

	This is a research release. The repository does not currently publish benchmark, baseline-comparison, or safety-evaluation results.

	## Lineage

	- Base model: [LiquidAI/LFM2.5-2.6B](https://huggingface.co/LiquidAI/LFM2.5-2.6B)
	- Dataset recorded in metadata: [Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset](https://huggingface.co/datasets/Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset)
	- Formats: Transformers / Safetensors and GGUF
	- License: Apache-2.0

	## Repository formats

	Transformers assets include `model.safetensors`, configuration files, tokenizer files, and a chat template.

	GGUF exports include:

	- `F16`
	- `Q8_0`
	- `Q4_K_M`

	Keeping both formats in one repository is convenient, but users should explicitly choose the path that matches their runtime.

	## Transformers usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "reaperdoesntknow/LFM2.5-2.6B-CyberSec"

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	device_map="auto",
	)

	messages = [
	{"role": "user", "content": "Explain defense in depth in plain language."}
	]
	inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
	).to(model.device)

	outputs = model.generate(**inputs, max_new_tokens=256)
	answer = outputs[0][inputs["input_ids"].shape[-1]:]
	print(tokenizer.decode(answer, skip_special_tokens=True))
	```

	The checked configuration includes a bitsandbytes quantization block. Pin and test the exact Transformers, Accelerate, bitsandbytes, and device environment you intend to use.

	## GGUF usage

	With a recent `llama.cpp` build:

	```bash
	llama-cli \
	-hf reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M \
	--jinja
	```

	With Ollama:

	```bash
	ollama run hf.co/reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M
	```

	## Intended use

	- Research on small-model responses to cybersecurity instruction prompts.
	- Local qualitative testing and format comparison.
	- Comparison with the unchanged LiquidAI base model.
	- Evaluation-harness and inference-runtime development.

	## Evaluation status

	The dataset tag and exported files are observed. Improved cybersecurity ability is not established by those facts alone.

	Evidence needed for a stronger release claim includes:

	- A held-out test split and unchanged-base baseline.
	- Named cybersecurity and general-capability benchmarks.
	- Reproducible harness, seed, prompts, and model revision hashes.
	- Safety, misuse, and hallucination evaluation.
	- Separate results for the Transformers model and each GGUF quantization.

	## Limitations and safety

	- The model can produce incorrect, outdated, insecure, or harmful instructions.
	- Cybersecurity material is inherently dual use.
	- The public files reviewed for this card do not document preprocessing, contamination checks, full training hyperparameters, or checkpoint-selection criteria.
	- Quantized builds can behave differently from the Transformers checkpoint.
	- Do not execute generated commands without review and isolation.
	- Do not use this model as the sole basis for incident response, vulnerability disclosure, access control, or other consequential decisions.

	Part of the [CIx cybersecurity model collection](https://huggingface.co/collections/reaperdoesntknow/cix-cybersecurity-models).
	<!-- cix-keeper-ts:2026-08-16T13:15:47Z -->