Instructions to use SupraLabs/Supra-Mini-v3-0.5M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SupraLabs/Supra-Mini-v3-0.5M with Transformers:

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

pipe = pipeline("text-generation", model="SupraLabs/Supra-Mini-v3-0.5M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SupraLabs/Supra-Mini-v3-0.5M")
model = AutoModelForCausalLM.from_pretrained("SupraLabs/Supra-Mini-v3-0.5M")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SupraLabs/Supra-Mini-v3-0.5M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SupraLabs/Supra-Mini-v3-0.5M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SupraLabs/Supra-Mini-v3-0.5M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SupraLabs/Supra-Mini-v3-0.5M

SGLang

How to use SupraLabs/Supra-Mini-v3-0.5M 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 "SupraLabs/Supra-Mini-v3-0.5M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SupraLabs/Supra-Mini-v3-0.5M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "SupraLabs/Supra-Mini-v3-0.5M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SupraLabs/Supra-Mini-v3-0.5M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SupraLabs/Supra-Mini-v3-0.5M with Docker Model Runner:
```
docker model run hf.co/SupraLabs/Supra-Mini-v3-0.5M
```

Supra-Mini-v3-0.5M / inference.py

LH-Tech-AI

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aedb965 verified 10 days ago

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	print("[*] Loading libraries...")
	import torch
	from transformers import LlamaForCausalLM, PreTrainedTokenizerFast

	model_path = "./Supra-Mini-v3-0.5M-FINAL"

	print("[*] Loading tokenizer...")
	tokenizer = PreTrainedTokenizerFast.from_pretrained(model_path)

	print("[*] Loading model...")
	model = LlamaForCausalLM.from_pretrained(model_path)
	model.eval()

	prompt = "The main concept of physics is "
	print(f"[*] Prompt: {prompt!r}")

	inputs = tokenizer(prompt, return_tensors="pt")

	with torch.no_grad():
	outputs = model.generate(
	input_ids=inputs["input_ids"],
	attention_mask=inputs["attention_mask"],
	max_new_tokens=150,
	do_sample=True,
	temperature=0.5,
	top_p=0.9,
	top_k=25,
	repetition_penalty=1.3,
	pad_token_id=tokenizer.pad_token_id,
	eos_token_id=tokenizer.eos_token_id,
	)

	print("[*] Output:", tokenizer.decode(outputs[0], skip_special_tokens=True))