Instructions to use adoslabs/liara-eurollm-1.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use adoslabs/liara-eurollm-1.7b 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 adoslabs/liara-eurollm-1.7b:F16
# Run inference directly in the terminal:
llama cli -hf adoslabs/liara-eurollm-1.7b:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf adoslabs/liara-eurollm-1.7b:F16
# Run inference directly in the terminal:
llama cli -hf adoslabs/liara-eurollm-1.7b: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 adoslabs/liara-eurollm-1.7b:F16
# Run inference directly in the terminal:
./llama-cli -hf adoslabs/liara-eurollm-1.7b: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 adoslabs/liara-eurollm-1.7b:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf adoslabs/liara-eurollm-1.7b:F16

Use Docker

docker model run hf.co/adoslabs/liara-eurollm-1.7b:F16

LM Studio
Jan

vLLM

How to use adoslabs/liara-eurollm-1.7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adoslabs/liara-eurollm-1.7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adoslabs/liara-eurollm-1.7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/adoslabs/liara-eurollm-1.7b:F16

Ollama
How to use adoslabs/liara-eurollm-1.7b with Ollama:
```
ollama run hf.co/adoslabs/liara-eurollm-1.7b:F16
```

Unsloth Studio

How to use adoslabs/liara-eurollm-1.7b 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 adoslabs/liara-eurollm-1.7b 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 adoslabs/liara-eurollm-1.7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for adoslabs/liara-eurollm-1.7b to start chatting

Docker Model Runner
How to use adoslabs/liara-eurollm-1.7b with Docker Model Runner:
```
docker model run hf.co/adoslabs/liara-eurollm-1.7b:F16
```

Lemonade

How to use adoslabs/liara-eurollm-1.7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull adoslabs/liara-eurollm-1.7b:F16

Run and chat with the model

lemonade run user.liara-eurollm-1.7b-F16

List all available models

lemonade list

Atomic Chat

Liara EuroLLM-1.7B (GGUF Q8_0)

Liara 1.7B EU "EU-native" 🇪🇺 — il modello europeo dell'app Liara (assistente personale locale e privata): nato dal progetto EU EuroLLM, italiano forte.

Fine-tuning LoRA (SFT + KTO) di utter-project/EuroLLM-1.7B-Instruct sul dominio Liara: conversazione naturale in italiano, memoria personale e tool-calling testuale ChatML (<tool_call>{"name": …, "arguments": …}</tool_call>) sui 30 strumenti dell'app (email, agenda, note, meteo, calcoli, web, file, peer, telefono).

Quantizzazione: Q8_0 (qualità piena, perdita impercettibile ~0,1% vs FP16 — regola di casa: mai sotto Q6).
Formato prompt: ChatML (<|im_start|>…<|im_end|>), system + blocco # Tools Hermes-style, gestito dall'app.
Allineamento anti-fabbricazione: KTO con negativi curati (meteo inventato, risposta-a-mente, memoria inventata, injection nei risultati tool).

Uso previsto

Questo GGUF è pensato per l'app Liara (zeli-local), che lo scarica dal catalogo e lo esegue con llama.cpp. Fuori dall'app: llama.cpp con prompt ChatML e il blocco tools nel system.

Licenza

Apache-2.0 (come il modello base EuroLLM).

Downloads last month: 80

GGUF

Model size

2B params

Architecture

llama

Hardware compatibility

8-bit

16-bit

Model tree for adoslabs/liara-eurollm-1.7b

Base model

utter-project/EuroLLM-1.7B

Finetuned

utter-project/EuroLLM-1.7B-Instruct

Quantized

(15)

this model