Instructions to use seerror-technologies/seerie-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 seerror-technologies/seerie-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 seerror-technologies/seerie-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf seerror-technologies/seerie-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 seerror-technologies/seerie-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf seerror-technologies/seerie-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 seerror-technologies/seerie-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf seerror-technologies/seerie-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 seerror-technologies/seerie-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf seerror-technologies/seerie-GGUF:Q4_K_M

Use Docker

docker model run hf.co/seerror-technologies/seerie-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use seerror-technologies/seerie-GGUF with Ollama:
```
ollama run hf.co/seerror-technologies/seerie-GGUF:Q4_K_M
```
Unsloth Desktop

How to use seerror-technologies/seerie-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf seerror-technologies/seerie-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": "seerror-technologies/seerie-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use seerror-technologies/seerie-GGUF with Docker Model Runner:
```
docker model run hf.co/seerror-technologies/seerie-GGUF:Q4_K_M
```

Lemonade

How to use seerror-technologies/seerie-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull seerror-technologies/seerie-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.seerie-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use seerror-technologies/seerie-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 seerror-technologies/seerie-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 seerror-technologies/seerie-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use seerror-technologies/seerie-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf seerror-technologies/seerie-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 "seerror-technologies/seerie-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"

Seerie Logo

SEERIE

The machine scams. Seerie sees.

Offline AI protection against digital fraud, built for India.

What is Seerie?

Seerie is an offline, on device AI assistant that helps Indians recognize and respond to digital fraud. UPI scams, phishing, digital arrest scams, and more, all without needing an internet connection. Your questions and data never leave your device.

This repo contains quantized GGUF versions of Seerie for use with llama.cpp, Ollama, and LM Studio.

The full precision (safetensors) version is available at seerror/seerie. The Seerie app itself is live now on Google Play.

What Seerie knows

Common UPI fraud patterns and how to react
Phishing SMS and call recognition
Digital arrest scam tactics
Aadhaar security, including locking, unlocking, and misuse prevention
Indian cyber law basics such as the IT Act and DPDP Act
Where to report: cybercrime.gov.in, 1930 helpline
Responds naturally in Hindi, English, and Hinglish

Example

User: Mera UPI se paise kat gaye, kya karoon?

Seerie: Sabse pehle turant apne bank ko call karke card/UPI block karayein.
Phir cybercrime.gov.in par complaint file karein ya 1930 helpline par call karein,
jitni jaldi report karenge, paise wapas milne ke chances utne zyada honge.
Apna transaction ID aur bank statement screenshot ready rakhein complaint ke liye.

Base model

Fine tuned from Qwen3 1.7B (Apache 2.0 licensed) using QLoRA, trained with the Unsloth framework for faster and more memory efficient training.

Available quantizations

File	Size	Notes
`seerie-q4_k_m.gguf`	approximately 1.1 GB	Recommended default, smallest and fastest, ideal for phones
`seerie-q6_k.gguf`	Coming soon	Higher quality, needs more RAM

How to run

LM Studio

Download the .gguf file
Load it in LM Studio
Leave the system prompt empty
Set temperature to 0.3 to 0.4 for consistent answers

Ollama

ollama create seerie -f Modelfile
ollama run seerie

Create a Modelfile pointing to the downloaded .gguf file. See Ollama's GGUF import docs.

llama.cpp

./llama-cli -m seerie-q4_k_m.gguf -p "Mera UPI se paise kat gaye, kya karoon?"

Limitations

Not a substitute for filing an official police or cybercrime complaint. Always direct urgent cases to 1930 or cybercrime.gov.in
May not have information on very recent scam patterns that emerged after training
Small model (1.7B), best for quick guidance rather than deep legal analysis

License

Apache 2.0, inherited from the base model.

Made in 🇮🇳 by Seerror

Downloads last month: 107

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

4-bit

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Model tree for seerror-technologies/seerie-GGUF

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Quantized

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seerror-technologies
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seerie-GGUF

SEERIE

What is Seerie?

What Seerie knows

Example

Base model

Available quantizations

How to run

Limitations

Links

License

Model tree for seerror-technologies/seerie-GGUF