Instructions to use Abiray/Assistant-100M-Guardian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abiray/Assistant-100M-Guardian with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Abiray/Assistant-100M-Guardian",
	filename="guardian-100m-f16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Abiray/Assistant-100M-Guardian with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Abiray/Assistant-100M-Guardian:F16
# Run inference directly in the terminal:
llama-cli -hf Abiray/Assistant-100M-Guardian:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Abiray/Assistant-100M-Guardian:F16
# Run inference directly in the terminal:
llama-cli -hf Abiray/Assistant-100M-Guardian: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 Abiray/Assistant-100M-Guardian:F16
# Run inference directly in the terminal:
./llama-cli -hf Abiray/Assistant-100M-Guardian: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 Abiray/Assistant-100M-Guardian:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/Assistant-100M-Guardian:F16

Use Docker

docker model run hf.co/Abiray/Assistant-100M-Guardian:F16

LM Studio
Jan

vLLM

How to use Abiray/Assistant-100M-Guardian with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abiray/Assistant-100M-Guardian"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abiray/Assistant-100M-Guardian",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Abiray/Assistant-100M-Guardian:F16

Ollama
How to use Abiray/Assistant-100M-Guardian with Ollama:
```
ollama run hf.co/Abiray/Assistant-100M-Guardian:F16
```

Unsloth Studio

How to use Abiray/Assistant-100M-Guardian 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 Abiray/Assistant-100M-Guardian 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 Abiray/Assistant-100M-Guardian to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Abiray/Assistant-100M-Guardian to start chatting

Docker Model Runner
How to use Abiray/Assistant-100M-Guardian with Docker Model Runner:
```
docker model run hf.co/Abiray/Assistant-100M-Guardian:F16
```

Lemonade

How to use Abiray/Assistant-100M-Guardian with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/Assistant-100M-Guardian:F16

Run and chat with the model

lemonade run user.Assistant-100M-Guardian-F16

List all available models

lemonade list

Assistant-100M-Guardian

This is a custom 124M parameter language model built from scratch on the Llama architecture. It is just an expirement model doesnot give answer.

Model Details

Architecture: Custom Llama-based (12 Layers, 12 Heads, 768 Dim)
Parameter Count: 124M
Format: Safetensors

Usage & Formatting

Because this model uses a custom architecture class (LlamaNano), you must initialize your local PyTorch class first, and then load these Safetensor weights into it.

Stop Sequence: It is critical to update your generation script to use <|endoftext|> as the stop sequence. This ensures the model cleanly ends its turn after responding.

Downloads last month: 62

GGUF

Model size

0.2B params

Architecture

llama

Hardware compatibility

16-bit