✨ LYRA AI Engine
👨💻 Author & Creator Profile
- Creator / Lead Engineer: Muhammad Taqi
- Model Identity: LYRA Core Engine
- Architecture Base: Causal Language Modeling
- Repository:
muhammad-taqi512/LYRA - License: Apache 2.0
"LYRA is designed as an autonomous, high-efficiency client-and-cloud native AI model engineered to deliver lightning-fast responses with precise ChatML structuring." — Muhammad Taqi
⚡ Key Capabilities & Features
- 🚀 Engineered by Muhammad Taqi: Tailored system execution for fast response streaming and low-latency inference.
- 🧠 ChatML Native Execution: Built to understand structured system persona directives and multi-turn conversational trees.
- 💻 Clean Code Generation: Precision output tuned for full-stack engineering, JavaScript, Python, and automated web setups.
- 🔒 Zero Third-Party Branding: Fully independent execution layer without runtime dependencies on external base models in application outputs.
🛠️ Usage Instructions
Python (transformers Integration)
Aap is model ko direct Muhammad Taqi's Hugging Face Repository se pull karke Python mein run kar sakte hain:
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse, HTMLResponse
from fastapi.middleware.cors import CORSMiddleware
from transformers import pipeline, AutoTokenizer
import datetime
app = FastAPI()
# CORS enabled for local/web connectivity
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Initialize Model (Aapka Model ID)
print("⏳ Loading LYRA Core Engine...")
model_id = 'muhammad-taqi512/LYRA'
tokenizer = AutoTokenizer.from_pretrained(model_id)
ai_pipeline = pipeline('text-generation', model=model_id, tokenizer=tokenizer)
print("✅ LYRA AI Engine Active!")
# Serve index.html UI at root route
@app.get("/", response_class=HTMLResponse)
async def serve_index():
with open("index.html", "r", encoding="utf-8") as f:
return f.read()
@app.post("/api/chat")
async def chat_endpoint(data: dict):
# Extracting parameters sent from frontend
user_message = data.get("message", "")
custom_rules = data.get(
"custom_rules",
"You are LYRA, an advanced AI created by Muhammad Taqi. You give accurate answers and clean code."
)
if not user_message:
return JSONResponse({"error": "Message is missing"}, status_code=400)
# Constructing prompt using incoming custom rules
full_prompt = f"<|im_start|>system\n{custom_rules}\n<|im_end|>\n<|im_start|>user\n{user_message}\n<|im_end|>\n<|im_start|>assistant\n"
# Model Generation
output = ai_pipeline(
full_prompt,
max_new_tokens=250,
temperature=0.7,
top_p=0.9,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
# Extract response
raw_text = output[0]['generated_text']
response_text = raw_text.split("<|im_start|>assistant\n")[-1].replace("<|im_end|>", "").strip()
return {
"status": "success",
"response": response_text
}