Instructions to use muhammad-taqi512/LYRAMOON with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use muhammad-taqi512/LYRAMOON with Transformers:

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

pipe = pipeline("text-generation", model="muhammad-taqi512/LYRAMOON")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("muhammad-taqi512/LYRAMOON")
model = AutoModelForCausalLM.from_pretrained("muhammad-taqi512/LYRAMOON", device_map="auto")
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 muhammad-taqi512/LYRAMOON with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "muhammad-taqi512/LYRAMOON"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "muhammad-taqi512/LYRAMOON",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/muhammad-taqi512/LYRAMOON

SGLang

How to use muhammad-taqi512/LYRAMOON 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 "muhammad-taqi512/LYRAMOON" \
    --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": "muhammad-taqi512/LYRAMOON",
		"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 "muhammad-taqi512/LYRAMOON" \
        --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": "muhammad-taqi512/LYRAMOON",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use muhammad-taqi512/LYRAMOON with Docker Model Runner:
```
docker model run hf.co/muhammad-taqi512/LYRAMOON
```

🌕 LYRAMOON Engine

Architected & Maintained by Muhammad Taqi

A next-generation 7B parameter reasoning engine offering high-speed intelligence, complex logic evaluation, and clean code generation.

👨‍💻 Creator Profile

Architect: Muhammad Taqi
Model ID: muhammad-taqi512/LYRAMOON
Target Behavior: Gemini-grade multi-turn conversational AI
Framework: PyTorch / Transformers

🛠️ Usage Example

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

MODEL_ID = "muhammad-taqi512/LYRAMOON"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(MODEL_ID)

lyra_moon = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "<|im_start|>system\nYou are LYRAMOON, created by Muhammad Taqi.<|im_end|>\n<|im_start|>user\nExplain quantum computing simply.<|im_end|>\n<|im_start|>assistant\n"
print(lyra_moon(prompt, max_new_tokens=200)[0]['generated_text'])

Downloads last month: 368

Safetensors

Model size

8B params

Tensor type

BF16