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

Libraries

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

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

pipe = pipeline("text-generation", model="utter-project/EuroLLM-1.7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("utter-project/EuroLLM-1.7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("utter-project/EuroLLM-1.7B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use utter-project/EuroLLM-1.7B-Instruct 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-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": "utter-project/EuroLLM-1.7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use utter-project/EuroLLM-1.7B-Instruct 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-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "utter-project/EuroLLM-1.7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "utter-project/EuroLLM-1.7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

phmartins commited on Sep 2, 2024

Commit

98c0afb

verified ·

1 Parent(s): b55a010

Update README.md

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README.md +5 -1

README.md CHANGED Viewed

@@ -145,4 +145,8 @@ Results show that EuroLLM-1.7B is superior to TinyLlama-v1.1 and similar to Gemm
 |--------------------|---------|---------|--------|---------|--------|---------|------------|---------|--------|--------|---------|--------|-----------|----------|-----------|--------|---------|
 | EuroLLM-1.7B | 0.4744  | 0.6084  | 0.4772  | 0.5310  | 0.5260  | 0.5067  | 0.5206  | 0.4674  | 0.4893   | 0.4075   | 0.4813   | 0.3605   | 0.4067   | 0.4598   | 0.4368   | 0.4700   | 0.4405   |
 | TinyLlama-v1.1       |0.3674   | 0.6248  | 0.3650  | 0.4137  | 0.4010  | 0.3780  | 0.3892  | 0.3494  | 0.3588   | 0.2880   | 0.3561   | 0.2841   | 0.3073   | 0.3267   | 0.3349   | 0.3408   | 0.3613   |
-| Gemma-2B             |0.4666  | 0.7165  | 0.4756  | 0.5414  | 0.5180  | 0.4841  | 0.5081  | 0.4664  | 0.4655   | 0.3868   | 0.4383   | 0.3413   | 0.3710   | 0.4316   | 0.4291   | 0.4471   | 0.4448   |

 |--------------------|---------|---------|--------|---------|--------|---------|------------|---------|--------|--------|---------|--------|-----------|----------|-----------|--------|---------|
 | EuroLLM-1.7B | 0.4744  | 0.6084  | 0.4772  | 0.5310  | 0.5260  | 0.5067  | 0.5206  | 0.4674  | 0.4893   | 0.4075   | 0.4813   | 0.3605   | 0.4067   | 0.4598   | 0.4368   | 0.4700   | 0.4405   |
 | TinyLlama-v1.1       |0.3674   | 0.6248  | 0.3650  | 0.4137  | 0.4010  | 0.3780  | 0.3892  | 0.3494  | 0.3588   | 0.2880   | 0.3561   | 0.2841   | 0.3073   | 0.3267   | 0.3349   | 0.3408   | 0.3613   |
+| Gemma-2B             |0.4666  | 0.7165  | 0.4756  | 0.5414  | 0.5180  | 0.4841  | 0.5081  | 0.4664  | 0.4655   | 0.3868   | 0.4383   | 0.3413   | 0.3710   | 0.4316   | 0.4291   | 0.4471   | 0.4448   |
+## Bias, Risks, and Limitations
+EuroLLM-1.7B-Instruct has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).