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
```

Ollama/llama.cpp support

#10

by dpreti - opened Feb 12, 2025

Discussion

dpreti

Almawave org Feb 12, 2025

We observed that at the moment the model is not served correctly with Ollama and llama.cpp .

We are currently investigating the reasons behind this unexpected behavior.
In the meanwhile we strongly suggest to serve the model using vLLM or the Transformer library as showed in the model card.

dpreti

Almawave org Feb 20, 2025

Velvet-14B model has been released on the ollama library. It can be used in its q4_K_M quantized version with the command

ollama run Almawave/velvet:14b

Other versions, including the q8_0, and fp16 are available and can be found here:
https://ollama.com/Almawave/Velvet

dpreti changed discussion status to closed Feb 20, 2025

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