Instructions to use Echelon-AI/marathi-llama3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Echelon-AI/marathi-llama3-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Echelon-AI/marathi-llama3-GGUF",
	filename="unsloth.BF16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Echelon-AI/marathi-llama3-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 Echelon-AI/marathi-llama3-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf Echelon-AI/marathi-llama3-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Echelon-AI/marathi-llama3-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf Echelon-AI/marathi-llama3-GGUF:BF16

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 Echelon-AI/marathi-llama3-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf Echelon-AI/marathi-llama3-GGUF:BF16

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 Echelon-AI/marathi-llama3-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Echelon-AI/marathi-llama3-GGUF:BF16

Use Docker

docker model run hf.co/Echelon-AI/marathi-llama3-GGUF:BF16

LM Studio
Jan
Ollama
How to use Echelon-AI/marathi-llama3-GGUF with Ollama:
```
ollama run hf.co/Echelon-AI/marathi-llama3-GGUF:BF16
```

Unsloth Studio

How to use Echelon-AI/marathi-llama3-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 Echelon-AI/marathi-llama3-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 Echelon-AI/marathi-llama3-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Echelon-AI/marathi-llama3-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Echelon-AI/marathi-llama3-GGUF with Docker Model Runner:
```
docker model run hf.co/Echelon-AI/marathi-llama3-GGUF:BF16
```

Lemonade

How to use Echelon-AI/marathi-llama3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Echelon-AI/marathi-llama3-GGUF:BF16

Run and chat with the model

lemonade run user.marathi-llama3-GGUF-BF16

List all available models

lemonade list

Meta llama3 8B trained on marathi alpaca cleaned for 1.5 epochs, On A100 40GB

Model Overview

Marathi-Llama3 is a fine-tuned version of the Llama3 model, tailored specifically for the Marathi language. This model leverages the power of the Llama3 architecture to provide accurate and nuanced responses in Marathi, opening up advanced AI capabilities to Marathi-speaking communities.

Downloads last month: 56

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

5-bit

8-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Echelon-AI
/

marathi-llama3-GGUF

Model Overview

Dataset used to train Echelon-AI/marathi-llama3-GGUF