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 15, 2024

Commit

e46f1d4

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1 Parent(s): 376fbaa

Update README.md

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@@ -16,7 +16,7 @@ tags:
 # Salamandra 7B aligned EADOP Model Card
 Salamandra 7B aligned EADOP is a full-finetuning version of
 [BSC Language Technologies Unit](https://huggingface.co/BSC-LT)'s
-[Salamndra 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.
@@ -36,7 +36,7 @@ capability to politely and informatively refuse to answer questions that are out
 ---
 ## Model Details
-Please refer to the [Salamndra Instruct 7B model details](https://huggingface.co/BSC-LT/salamandra-7b-instruct#model-details)
 for the specific details about the model architecture and pretraining.
 ## Intended Use
@@ -55,9 +55,9 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 import transformers
 import torch
-model_id = "BSC-LT/salamandra-7b-instruct"
-text = "At what temperature does water boil?"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(

 # Salamandra 7B aligned EADOP Model Card
 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.
 ---
 ## Model Details
+Please refer to the [Salamandra Instruct 7B model details](https://huggingface.co/BSC-LT/salamandra-7b-instruct#model-details)
 for the specific details about the model architecture and pretraining.
 ## Intended Use
 import transformers
 import torch
+model_id = "projecte-aina/salamandra-7b-aligned-EADOP"
+text = "Quina és la finalitat del Servei Meterològic de Catalunya ?"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(