Instructions to use sbapan41/Quantumhash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sbapan41/Quantumhash with Transformers:

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

pipe = pipeline("text-generation", model="sbapan41/Quantumhash")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sbapan41/Quantumhash")
model = AutoModelForCausalLM.from_pretrained("sbapan41/Quantumhash")
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

vLLM

How to use sbapan41/Quantumhash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sbapan41/Quantumhash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sbapan41/Quantumhash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sbapan41/Quantumhash

SGLang

How to use sbapan41/Quantumhash 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 "sbapan41/Quantumhash" \
    --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": "sbapan41/Quantumhash",
		"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 "sbapan41/Quantumhash" \
        --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": "sbapan41/Quantumhash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sbapan41/Quantumhash with Docker Model Runner:
```
docker model run hf.co/sbapan41/Quantumhash
```

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🚀 Quantumhash

This is a Quantumhash trained for text generation.
You can use it to as a text_generation Model.

🔥 Try It Now

Use the inference widget below or in your own application.

🚀 How to Use This Model

🔹 Use in Python

For text models (Transformers):

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "sbapan41/Quantumhash"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Once upon a time..."
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs)
print(tokenizer.decode(output[0]))

---
## 🌍 Inference API
### Try the model directly in your browser with the Hugging Face Inference API.
---
from huggingface_hub import InferenceClient

client = InferenceClient(model="sbapan41/Quantumhash")
response = client.text_generation("Hello, how are you?")
print(response)

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Safetensors

Model size

7B params

Tensor type

BF16

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