Instructions to use QuantaSparkLabs/Quantum-X with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantaSparkLabs/Quantum-X with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuantaSparkLabs/Quantum-X")
model = AutoModelForCausalLM.from_pretrained("QuantaSparkLabs/Quantum-X")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use QuantaSparkLabs/Quantum-X with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/QuantaSparkLabs/Quantum-X

SGLang

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

Docker Model Runner
How to use QuantaSparkLabs/Quantum-X with Docker Model Runner:
```
docker model run hf.co/QuantaSparkLabs/Quantum-X
```

QuantaSparkLabs commited on 10 days ago

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@@ -61,13 +61,6 @@ Quantum‑X is a **0.5 billion parameter** language model developed by QuantaSpa
 - **Fast Inference**: 0.5B parameters = near‑instant responses on CPU or GPU.
 - **Edge Friendly**: Runs comfortably on 2 GB RAM, even on a phone.
-## 🧠 Identity
-Quantum‑X knows exactly who it is:
-> *"I'm Quantum‑X, a helpful AI built by QuantaSparkLabs."*
-It never claims to be Qwen, GPT, or any other model.
 ## 💻 Quick Start
 ```python

 - **Fast Inference**: 0.5B parameters = near‑instant responses on CPU or GPU.
 - **Edge Friendly**: Runs comfortably on 2 GB RAM, even on a phone.
 ## 💻 Quick Start
 ```python