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

Libraries

How to use ataeff/g with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-270m-it")
model = PeftModel.from_pretrained(base_model, "ataeff/g")

llama-cpp-python

How to use ataeff/g with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ataeff/g",
	filename="leo-1b-plain-q4.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ataeff/g with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/ataeff/g:F16

LM Studio
Jan

vLLM

How to use ataeff/g with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ataeff/g:F16

Ollama
How to use ataeff/g with Ollama:
```
ollama run hf.co/ataeff/g:F16
```

Unsloth Studio new

How to use ataeff/g 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 ataeff/g 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 ataeff/g to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Lemonade

How to use ataeff/g with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ataeff/g:F16

Run and chat with the model

lemonade run user.g-F16

List all available models

lemonade list

File size: 977 Bytes

0e74c64

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  "alpha_pattern": {},
  "arrow_config": null,
  "auto_mapping": null,
  "base_model_name_or_path": "unsloth/gemma-3-270m-it",
  "bias": "none",
  "corda_config": null,
  "ensure_weight_tying": false,
  "eva_config": null,
  "exclude_modules": null,
  "fan_in_fan_out": false,
  "inference_mode": true,
  "init_lora_weights": true,
  "layer_replication": null,
  "layers_pattern": null,
  "layers_to_transform": null,
  "loftq_config": {},
  "lora_alpha": 32,
  "lora_bias": false,
  "lora_dropout": 0.05,
  "megatron_config": null,
  "megatron_core": "megatron.core",
  "modules_to_save": null,
  "peft_type": "LORA",
  "peft_version": "0.18.1",
  "qalora_group_size": 16,
  "r": 16,
  "rank_pattern": {},
  "revision": null,
  "target_modules": [
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  ],
  "target_parameters": null,
  "task_type": "CAUSAL_LM",
  "trainable_token_indices": null,
  "use_dora": false,
  "use_qalora": false,
  "use_rslora": false
}