Instructions to use MalithaBandara/EleGuard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MalithaBandara/EleGuard with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MalithaBandara/EleGuard",
	filename="gemma-4-e2b-it.F16-mmproj.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use MalithaBandara/EleGuard with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MalithaBandara/EleGuard:F16
# Run inference directly in the terminal:
llama-cli -hf MalithaBandara/EleGuard:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MalithaBandara/EleGuard:F16
# Run inference directly in the terminal:
llama-cli -hf MalithaBandara/EleGuard:F16

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 MalithaBandara/EleGuard:F16
# Run inference directly in the terminal:
./llama-cli -hf MalithaBandara/EleGuard:F16

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 MalithaBandara/EleGuard:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MalithaBandara/EleGuard:F16

Use Docker

docker model run hf.co/MalithaBandara/EleGuard:F16

LM Studio
Jan
Ollama
How to use MalithaBandara/EleGuard with Ollama:
```
ollama run hf.co/MalithaBandara/EleGuard:F16
```

Unsloth Studio new

How to use MalithaBandara/EleGuard 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 MalithaBandara/EleGuard 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 MalithaBandara/EleGuard to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MalithaBandara/EleGuard to start chatting

Docker Model Runner
How to use MalithaBandara/EleGuard with Docker Model Runner:
```
docker model run hf.co/MalithaBandara/EleGuard:F16
```

Lemonade

How to use MalithaBandara/EleGuard with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MalithaBandara/EleGuard:F16

Run and chat with the model

lemonade run user.EleGuard-F16

List all available models

lemonade list

MalithaBandara commited on 12 days ago

Commit

89444a9

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1 Parent(s): 7654f26

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@@ -29,8 +29,8 @@ tags:
 The core breakthrough of EleGuard is the shift from simple classification to **expert reasoning**. Instead of training only on labels, the model was fine-tuned on "thought blocks" generated by a Teacher model (Gemini 3.1 Flash).
 For every image or audio sample, the model is trained to explain its reasoning—such as identifying thermal signatures in thick brush or frequency patterns in a rumble—before outputting a final status:
-*   **🚨 ALERT:** Elephant presence confirmed.
-*   **✅ SAFE:** No threat detected.
 ## Dataset Details
 The model was trained on a curated dataset of **2,600 samples** organized into:

 The core breakthrough of EleGuard is the shift from simple classification to **expert reasoning**. Instead of training only on labels, the model was fine-tuned on "thought blocks" generated by a Teacher model (Gemini 3.1 Flash).
 For every image or audio sample, the model is trained to explain its reasoning—such as identifying thermal signatures in thick brush or frequency patterns in a rumble—before outputting a final status:
+*   **ALERT:** Elephant presence confirmed.
+*   **SAFE:** No threat detected.
 ## Dataset Details
 The model was trained on a curated dataset of **2,600 samples** organized into: