Instructions to use utter-project/EuroLLM-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use utter-project/EuroLLM-1.7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="utter-project/EuroLLM-1.7B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("utter-project/EuroLLM-1.7B")
model = AutoModelForCausalLM.from_pretrained("utter-project/EuroLLM-1.7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use utter-project/EuroLLM-1.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "utter-project/EuroLLM-1.7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "utter-project/EuroLLM-1.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/utter-project/EuroLLM-1.7B

SGLang

How to use utter-project/EuroLLM-1.7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "utter-project/EuroLLM-1.7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "utter-project/EuroLLM-1.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "utter-project/EuroLLM-1.7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "utter-project/EuroLLM-1.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use utter-project/EuroLLM-1.7B with Docker Model Runner:
```
docker model run hf.co/utter-project/EuroLLM-1.7B
```

phmartins commited on Sep 24, 2024

Commit

081f92a

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@@ -123,7 +123,7 @@ The results show that EuroLLM-1.7B is substantially better than Gemma-2B in Mach
 #### WMT-24
-| Model | AVG | AVG en-xx | AVG xx-xx | en-es-latam | en-cs | en-ru | en-uk | en-ja | en-zh-cn | en-hi | cs-uk | ja-zh-cn |
 |---------|------|------|-------|-------|-------|-------|--------|--------|-------|-------|-------|-----|
 | EuroLLM-1.7B-Instruct|79.32     | 79.32     | 79.34     | 79.42     | 80.67     | 80.55     | 78.65     | 80.12     | 82.96     | 80.60      | 71.59      | 83.48      | 75.20      |
 |Gemma-2B-EuroBlocks| 74.72     | 74.41     | 75.97     | 74.93     | 78.81     | 70.54     | 74.90     | 75.84     | 79.48     | 78.06      | 62.70      | 79.87      | 72.07      |

 #### WMT-24
+| Model | AVG | AVG en-xx | AVG xx-xx | en-de | en-es-latam | en-cs | en-ru | en-uk | en-ja | en-zh-cn | en-hi | cs-uk | ja-zh-cn |
 |---------|------|------|-------|-------|-------|-------|--------|--------|-------|-------|-------|-----|
 | EuroLLM-1.7B-Instruct|79.32     | 79.32     | 79.34     | 79.42     | 80.67     | 80.55     | 78.65     | 80.12     | 82.96     | 80.60      | 71.59      | 83.48      | 75.20      |
 |Gemma-2B-EuroBlocks| 74.72     | 74.41     | 75.97     | 74.93     | 78.81     | 70.54     | 74.90     | 75.84     | 79.48     | 78.06      | 62.70      | 79.87      | 72.07      |