Instructions to use tiararodney/EuroLLM-9B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiararodney/EuroLLM-9B-Instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tiararodney/EuroLLM-9B-Instruct",
	filename="EuroLLM-9B-Instruct-Q4_K_M.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 tiararodney/EuroLLM-9B-Instruct 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 tiararodney/EuroLLM-9B-Instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tiararodney/EuroLLM-9B-Instruct:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tiararodney/EuroLLM-9B-Instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tiararodney/EuroLLM-9B-Instruct:Q4_K_M

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 tiararodney/EuroLLM-9B-Instruct:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tiararodney/EuroLLM-9B-Instruct:Q4_K_M

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 tiararodney/EuroLLM-9B-Instruct:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tiararodney/EuroLLM-9B-Instruct:Q4_K_M

Use Docker

docker model run hf.co/tiararodney/EuroLLM-9B-Instruct:Q4_K_M

LM Studio
Jan

vLLM

How to use tiararodney/EuroLLM-9B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiararodney/EuroLLM-9B-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": "tiararodney/EuroLLM-9B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiararodney/EuroLLM-9B-Instruct:Q4_K_M

Ollama
How to use tiararodney/EuroLLM-9B-Instruct with Ollama:
```
ollama run hf.co/tiararodney/EuroLLM-9B-Instruct:Q4_K_M
```

Unsloth Studio

How to use tiararodney/EuroLLM-9B-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 tiararodney/EuroLLM-9B-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 tiararodney/EuroLLM-9B-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tiararodney/EuroLLM-9B-Instruct to start chatting

Atomic Chat new
Docker Model Runner
How to use tiararodney/EuroLLM-9B-Instruct with Docker Model Runner:
```
docker model run hf.co/tiararodney/EuroLLM-9B-Instruct:Q4_K_M
```

Lemonade

How to use tiararodney/EuroLLM-9B-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tiararodney/EuroLLM-9B-Instruct:Q4_K_M

Run and chat with the model

lemonade run user.EuroLLM-9B-Instruct-Q4_K_M

List all available models

lemonade list

EuroLLM-9B-Instruct GGUF

GGUF quantizations of utter-project/EuroLLM-9B-Instruct, converted from the original bf16 weights with llama.cpp.

Prepared as a subject for the sek scrollback-priming cross-model study, where EuroLLM-9B stands in as the distributionally distant baseline: a multilingual European-language model rather than an English- and code-centric one. The question it probes is whether synthetic-scrollback priming can hold a model whose training mass is natural-language prose in consistent POSIX shell syntax.

Provenance

Source: utter-project/EuroLLM-9B-Instruct (bf16 safetensors)
Converted: llama.cpp convert_hf_to_gguf.py --outtype bf16
Quantized: llama.cpp llama-quantize
Built on a single Tesla V100-SXM2-32GB

Quants

File	Quant	Size
filled after the build

Prompt format

ChatML, with a system role. Stop token: the im-end token.

Downloads last month: 85

GGUF

Model size

9B params

Architecture

llama

Hardware compatibility

4-bit

16-bit

Model tree for tiararodney/EuroLLM-9B-Instruct

Base model

utter-project/EuroLLM-9B

Finetuned

utter-project/EuroLLM-9B-Instruct

Quantized

(15)

this model