Instructions to use Ashraya/gemma-270m-legal-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ashraya/gemma-270m-legal-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Ashraya/gemma-270m-legal-gguf",
	filename="gemma-270m-q4km-optimized.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 Ashraya/gemma-270m-legal-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ashraya/gemma-270m-legal-gguf
# Run inference directly in the terminal:
llama-cli -hf Ashraya/gemma-270m-legal-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ashraya/gemma-270m-legal-gguf
# Run inference directly in the terminal:
llama-cli -hf Ashraya/gemma-270m-legal-gguf

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 Ashraya/gemma-270m-legal-gguf
# Run inference directly in the terminal:
./llama-cli -hf Ashraya/gemma-270m-legal-gguf

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 Ashraya/gemma-270m-legal-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ashraya/gemma-270m-legal-gguf

Use Docker

docker model run hf.co/Ashraya/gemma-270m-legal-gguf

LM Studio
Jan
Ollama
How to use Ashraya/gemma-270m-legal-gguf with Ollama:
```
ollama run hf.co/Ashraya/gemma-270m-legal-gguf
```

Unsloth Studio

How to use Ashraya/gemma-270m-legal-gguf 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 Ashraya/gemma-270m-legal-gguf 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 Ashraya/gemma-270m-legal-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ashraya/gemma-270m-legal-gguf to start chatting

Docker Model Runner
How to use Ashraya/gemma-270m-legal-gguf with Docker Model Runner:
```
docker model run hf.co/Ashraya/gemma-270m-legal-gguf
```

Lemonade

How to use Ashraya/gemma-270m-legal-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ashraya/gemma-270m-legal-gguf

Run and chat with the model

lemonade run user.gemma-270m-legal-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Gemma 3 270M - Legal Auto-Completion (GGUF)

Fine-tuned Gemma 3 270M for legal text auto-completion, optimized for client-side browser deployment.

Model Details

Format: GGUF (Q4_K_M quantization with Q5_0 fallback)
Size: ~169 MB
Optimized for: Wllama (WebAssembly) browser inference

Usage with llama.cpp

./llama-cli -m gemma-270m-q4km-optimized.gguf -p "The court held that" -n 30

Usage with Wllama (Browser)

import { Wllama } from '@wllama/wllama';

const CONFIG_PATHS = {
    'single-thread/wllama.wasm': 'path/to/single-thread/wllama.wasm',
    'multi-thread/wllama.wasm': 'path/to/multi-thread/wllama.wasm',
};

const wllama = new Wllama(CONFIG_PATHS);
await wllama.loadModelFromHF(
    'Ashraya/gemma-270m-legal-gguf',
    'gemma-270m-q4km-optimized.gguf'
);

const result = await wllama.createCompletion('The court held that', {
    nPredict: 20,
    sampling: { temp: 0.3, top_p: 0.95 }
});
console.log(result);

Files

gemma-270m-q4km-optimized.gguf - Main model file (~169MB)

Downloads last month: 2

GGUF

Model size

0.3B params

Architecture

gemma3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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