Instructions to use projecte-aina/salamandra-7b-aligned-EADOP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use projecte-aina/salamandra-7b-aligned-EADOP with Transformers:

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

pipe = pipeline("text-generation", model="projecte-aina/salamandra-7b-aligned-EADOP")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("projecte-aina/salamandra-7b-aligned-EADOP")
model = AutoModelForCausalLM.from_pretrained("projecte-aina/salamandra-7b-aligned-EADOP", 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 projecte-aina/salamandra-7b-aligned-EADOP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "projecte-aina/salamandra-7b-aligned-EADOP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "projecte-aina/salamandra-7b-aligned-EADOP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/projecte-aina/salamandra-7b-aligned-EADOP

SGLang

How to use projecte-aina/salamandra-7b-aligned-EADOP 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 "projecte-aina/salamandra-7b-aligned-EADOP" \
    --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": "projecte-aina/salamandra-7b-aligned-EADOP",
		"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 "projecte-aina/salamandra-7b-aligned-EADOP" \
        --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": "projecte-aina/salamandra-7b-aligned-EADOP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use projecte-aina/salamandra-7b-aligned-EADOP with Docker Model Runner:
```
docker model run hf.co/projecte-aina/salamandra-7b-aligned-EADOP
```

MarinaPlius commited on Oct 16, 2024

Commit

be4f549

verified ·

1 Parent(s): e46f1d4

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@@ -17,7 +17,7 @@ tags:
 Salamandra 7B aligned EADOP is a full-finetuning version of
 [BSC Language Technologies Unit](https://huggingface.co/BSC-LT)'s
 [Salamandra Instruct 7B](https://huggingface.co/BSC-LT/salamandra-7b-instruct)
-model by the at the Barcelona Supercomputing Center focused on improving
 the handling of out-of-domain Questions in a RAG instruction-following setting.
 The model has been finetuned on a dataset consisting of 2,000+ human annotated in-
@@ -88,14 +88,17 @@ Using this template, each turn is preceded by a `<|im_start|>` delimiter and the
 ## Finetuning Data
 Please refer to [alinia/EADOP-RAG-out-of-domain](https://huggingface.co/datasets/alinia/EADOP-RAG-out-of-domain) for the Dataset Card.
 ### Author
 This model has been finetuned by [Alinia AI](https://alinia.ai/).
 ### Contact
-For further information, please email [contact@alinia.ai](mailto:contact@alinia.ai).
 ### Acknowledgements
 This project is part of a partnership with the Language Technologies Unit at the [Barcelona Supercomputing Center](https://www.bsc.es/).

 Salamandra 7B aligned EADOP is a full-finetuning version of
 [BSC Language Technologies Unit](https://huggingface.co/BSC-LT)'s
 [Salamandra Instruct 7B](https://huggingface.co/BSC-LT/salamandra-7b-instruct)
+model of the Barcelona Supercomputing Center focused on improving
 the handling of out-of-domain Questions in a RAG instruction-following setting.
 The model has been finetuned on a dataset consisting of 2,000+ human annotated in-
 ## Finetuning Data
 Please refer to [alinia/EADOP-RAG-out-of-domain](https://huggingface.co/datasets/alinia/EADOP-RAG-out-of-domain) for the Dataset Card.
 ### Author
 This model has been finetuned by [Alinia AI](https://alinia.ai/).
 ### Contact
+For further information, please email [langtech@bsc.es](mailto:langtech@bsc.es).
+### Copyright
+Copyright(c) 2024 by Language Technologies Unit, Barcelona Supercomputing Center.
+### License
+Apache-2.0
 ### Acknowledgements
 This project is part of a partnership with the Language Technologies Unit at the [Barcelona Supercomputing Center](https://www.bsc.es/).