Instructions to use VTXAI/vortex-50m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VTXAI/vortex-50m with Transformers:

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

pipe = pipeline("text-generation", model="VTXAI/vortex-50m", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("VTXAI/vortex-50m", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VTXAI/vortex-50m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VTXAI/vortex-50m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VTXAI/vortex-50m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VTXAI/vortex-50m

SGLang

How to use VTXAI/vortex-50m 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 "VTXAI/vortex-50m" \
    --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": "VTXAI/vortex-50m",
		"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 "VTXAI/vortex-50m" \
        --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": "VTXAI/vortex-50m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VTXAI/vortex-50m with Docker Model Runner:
```
docker model run hf.co/VTXAI/vortex-50m
```

Abhaykoul commited on 7 days ago

Commit

e196cdc

verified ·

1 Parent(s): cc6ed83

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -12,7 +12,7 @@ tags:
 - sft
 ---
-# Vortex-50M (Cybersecurity & Cryptography Specialist - Low Perplexity Edition)
 **Vortex-50M** is an on-device, sub-50M parameter decoder-only transformer with a 16,384-token vocabulary, fine-tuned (SFT) on [`VTXAI/cyber-crypto-balanced-qa`](https://huggingface.co/datasets/VTXAI/cyber-crypto-balanced-qa) from the base pretrained model [`VTXAI/vortex-50m-16k`](https://huggingface.co/VTXAI/vortex-50m-16k).

 - sft
 ---
+# Vortex-50M (Cybersecurity & Cryptography Specialist)
 **Vortex-50M** is an on-device, sub-50M parameter decoder-only transformer with a 16,384-token vocabulary, fine-tuned (SFT) on [`VTXAI/cyber-crypto-balanced-qa`](https://huggingface.co/datasets/VTXAI/cyber-crypto-balanced-qa) from the base pretrained model [`VTXAI/vortex-50m-16k`](https://huggingface.co/VTXAI/vortex-50m-16k).