Instructions to use Delcos/Velara with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Delcos/Velara with Transformers:

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

pipe = pipeline("text-generation", model="Delcos/Velara")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Delcos/Velara")
model = AutoModelForMultimodalLM.from_pretrained("Delcos/Velara")
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 Delcos/Velara with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Delcos/Velara

SGLang

How to use Delcos/Velara 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 "Delcos/Velara" \
    --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": "Delcos/Velara",
		"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 "Delcos/Velara" \
        --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": "Delcos/Velara",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Delcos/Velara with Docker Model Runner:
```
docker model run hf.co/Delcos/Velara
```

addons ?

by repjhonblow - opened Dec 12, 2023

Discussion

repjhonblow

Dec 12, 2023

Hi everybody.
I'm confused about the whole addons thing. Can someone clarify what doest it mean, which are available and so on ?

I've tried the suggested template/prompt but very soon the model goes all over the place.
Thanks

Delcos

Owner Dec 13, 2023

The "addons" are something that it can see when added to the pretext and are optional. It can push the response in a direction you want or "change her personality". They're whatever you type in under the "addons" sections. Make sure the format is the same and include each name in '. They can technically be anything, but it's best to use things like 'Happiness booster v2.7' or 'Dictionary Plus v7.6'.

Delcos

Owner Dec 13, 2023

Also check the updating settings. The guidance scale helps especially when starting a new conversation without the entire context being filled.

Delcos changed discussion status to closed Jan 3, 2024

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