Instructions to use la2ydev/RoLlama3.1-8b-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use la2ydev/RoLlama3.1-8b-Instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="la2ydev/RoLlama3.1-8b-Instruct",
	filename="RoLlama3.1-8b-Instruct-Q6_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use la2ydev/RoLlama3.1-8b-Instruct with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf la2ydev/RoLlama3.1-8b-Instruct:Q6_K
# Run inference directly in the terminal:
llama-cli -hf la2ydev/RoLlama3.1-8b-Instruct:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf la2ydev/RoLlama3.1-8b-Instruct:Q6_K
# Run inference directly in the terminal:
llama-cli -hf la2ydev/RoLlama3.1-8b-Instruct:Q6_K

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 la2ydev/RoLlama3.1-8b-Instruct:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf la2ydev/RoLlama3.1-8b-Instruct:Q6_K

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 la2ydev/RoLlama3.1-8b-Instruct:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf la2ydev/RoLlama3.1-8b-Instruct:Q6_K

Use Docker

docker model run hf.co/la2ydev/RoLlama3.1-8b-Instruct:Q6_K

LM Studio
Jan

vLLM

How to use la2ydev/RoLlama3.1-8b-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "la2ydev/RoLlama3.1-8b-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "la2ydev/RoLlama3.1-8b-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/la2ydev/RoLlama3.1-8b-Instruct:Q6_K

Ollama
How to use la2ydev/RoLlama3.1-8b-Instruct with Ollama:
```
ollama run hf.co/la2ydev/RoLlama3.1-8b-Instruct:Q6_K
```

Unsloth Studio

How to use la2ydev/RoLlama3.1-8b-Instruct 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 la2ydev/RoLlama3.1-8b-Instruct 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 la2ydev/RoLlama3.1-8b-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for la2ydev/RoLlama3.1-8b-Instruct to start chatting

Docker Model Runner
How to use la2ydev/RoLlama3.1-8b-Instruct with Docker Model Runner:
```
docker model run hf.co/la2ydev/RoLlama3.1-8b-Instruct:Q6_K
```

Lemonade

How to use la2ydev/RoLlama3.1-8b-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull la2ydev/RoLlama3.1-8b-Instruct:Q6_K

Run and chat with the model

lemonade run user.RoLlama3.1-8b-Instruct-Q6_K

List all available models

lemonade list

RoLlama3.1-8b-Instruct

This repository contains quantized versions of the model.

For the original model, please visit: OpenLLM-Ro/RoLlama3.1-8b-Instruct.

Intended Use

Intended Use Cases

RoLlama3.1 is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat.

Out-of-Scope Use

Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian.

Downloads last month: 2

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

6-bit

8-bit

Model tree for la2ydev/RoLlama3.1-8b-Instruct

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

OpenLLM-Ro/RoLlama3.1-8b-Instruct

Quantized

(3)

this model