Instructions to use Almawave/Velvet-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Almawave/Velvet-14B with Transformers:

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

pipe = pipeline("text-generation", model="Almawave/Velvet-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Almawave/Velvet-14B")
model = AutoModelForCausalLM.from_pretrained("Almawave/Velvet-14B")
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 Almawave/Velvet-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Almawave/Velvet-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Almawave/Velvet-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Almawave/Velvet-14B

SGLang

How to use Almawave/Velvet-14B 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 "Almawave/Velvet-14B" \
    --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": "Almawave/Velvet-14B",
		"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 "Almawave/Velvet-14B" \
        --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": "Almawave/Velvet-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Almawave/Velvet-14B with Docker Model Runner:
```
docker model run hf.co/Almawave/Velvet-14B
```

Inference Providers

by dbxbxn - opened Feb 1, 2025

Discussion

dbxbxn

Feb 1, 2025

I think it would be great to make your model accessible online through platforms like Hugging Face or cloud-based inference providers. This will not only increase its visibility but also make it easier for others to use and benefit from your work. Platforms like Hugging Face are very developer-friendly and offer tools to deploy models quickly. Plus, you can gather valuable feedback from the community to further improve your model.

xdatap1

Feb 3, 2025

I do agree, it would be useful to be able to test it with serverless APIs

vincenzo-sciacca-fox

Almawave org Feb 4, 2025

•

edited Feb 4, 2025

I think it would be great to make your model accessible online through platforms like Hugging Face or cloud-based inference providers. This will not only increase its visibility but also make it easier for others to use and benefit from your work. Platforms like Hugging Face are very developer-friendly and offer tools to deploy models quickly. Plus, you can gather valuable feedback from the community to further improve your model.

We have something in mind, we will give you the details as soon as we'll have some news

nickname100231

Feb 12, 2025

Alternatively, maybe you can deploy it here?

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