Text Generation
Transformers
Safetensors
PyTorch
English
gpt2
hardware-bus
memory-augmented
toolformer
slm
autonomous-agent
ssd-memory
edge-ai
text-generation-inference
Instructions to use AvinashRicky/AViGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AvinashRicky/AViGPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AvinashRicky/AViGPT")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AvinashRicky/AViGPT") model = AutoModelForCausalLM.from_pretrained("AvinashRicky/AViGPT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AvinashRicky/AViGPT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AvinashRicky/AViGPT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AvinashRicky/AViGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AvinashRicky/AViGPT
- SGLang
How to use AvinashRicky/AViGPT 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 "AvinashRicky/AViGPT" \ --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": "AvinashRicky/AViGPT", "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 "AvinashRicky/AViGPT" \ --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": "AvinashRicky/AViGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AvinashRicky/AViGPT with Docker Model Runner:
docker model run hf.co/AvinashRicky/AViGPT
| """ | |
| AViGPT v2: Hardware SSD Memory Controller (Component 3) | |
| ------------------------------------------------------ | |
| Provides ultra-fast (sub-millisecond) persistent memory storage and retrieval | |
| directly from local NVMe SSD storage using SQLite FTS5 (Full-Text Search). | |
| Creator & Owner: Avinash Ricky Yadlapalli | |
| """ | |
| import os | |
| import sqlite3 | |
| import time | |
| from typing import List, Dict, Any, Optional | |
| DEFAULT_DB_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "avigpt_ssd_memory.db") | |
| class SSDMemoryEngine: | |
| """Ultra-low latency SSD Memory Store with FTS5 BM25 search.""" | |
| def __init__(self, db_path: str = DEFAULT_DB_PATH): | |
| self.db_path = db_path | |
| self._init_db() | |
| def _get_connection(self) -> sqlite3.Connection: | |
| conn = sqlite3.connect(self.db_path, timeout=10.0) | |
| # WAL mode enables concurrent reads without locking and sub-millisecond disk access | |
| conn.execute("PRAGMA journal_mode=WAL;") | |
| conn.execute("PRAGMA synchronous=NORMAL;") | |
| conn.execute("PRAGMA cache_size=-64000;") # 64MB memory page cache | |
| return conn | |
| def _init_db(self): | |
| with self._get_connection() as conn: | |
| # Create FTS5 virtual table for lightning-fast keyword & semantic token retrieval | |
| conn.execute(""" | |
| CREATE VIRTUAL TABLE IF NOT EXISTS ssd_knowledge USING fts5( | |
| title, | |
| content, | |
| domain, | |
| tokenize='porter unicode61' | |
| ); | |
| """) | |
| conn.commit() | |
| def store(self, title: str, content: str, domain: str = "General") -> bool: | |
| """Stores a new fact directly into local SSD storage.""" | |
| try: | |
| with self._get_connection() as conn: | |
| conn.execute( | |
| "INSERT INTO ssd_knowledge (title, content, domain) VALUES (?, ?, ?);", | |
| (title.strip(), content.strip(), domain.strip()) | |
| ) | |
| conn.commit() | |
| return True | |
| except Exception as e: | |
| print(f"[SSD Memory Error] Failed to store: {e}") | |
| return False | |
| def query(self, query_str: str, top_k: int = 1) -> Optional[str]: | |
| """ | |
| Executes sub-millisecond full-text search against SSD storage. | |
| Returns top matching payload. | |
| """ | |
| words = [w for w in query_str.replace("'", " ").replace('"', " ").replace("-", " ").split() if len(w) > 2] | |
| if not words: | |
| words = query_str.strip().split() | |
| fts_query = " OR ".join(words) | |
| try: | |
| with self._get_connection() as conn: | |
| cursor = conn.cursor() | |
| # Query with BM25 ranking via OR disjunction | |
| cursor.execute( | |
| """ | |
| SELECT content, rank | |
| FROM ssd_knowledge | |
| WHERE ssd_knowledge MATCH ? | |
| ORDER BY rank | |
| LIMIT ?; | |
| """, | |
| (fts_query, top_k) | |
| ) | |
| rows = cursor.fetchall() | |
| if rows: | |
| return rows[0][0] | |
| # Fallback LIKE query if FTS had no hit | |
| cursor.execute( | |
| """ | |
| SELECT content | |
| FROM ssd_knowledge | |
| WHERE content LIKE ? OR title LIKE ? | |
| LIMIT 1; | |
| """, | |
| (f"%{words[0]}%", f"%{words[0]}%") | |
| ) | |
| fb_rows = cursor.fetchall() | |
| if fb_rows: | |
| return fb_rows[0][0] | |
| return None | |
| except Exception as e: | |
| print(f"[SSD Memory Query Error] {e}") | |
| return None | |
| def seed_initial_knowledge(self): | |
| """Seeds foundational knowledge and owner lineage into SSD storage.""" | |
| with self._get_connection() as conn: | |
| cursor = conn.cursor() | |
| cursor.execute("SELECT COUNT(*) FROM ssd_knowledge;") | |
| count = cursor.fetchone()[0] | |
| if count > 0: | |
| print(f"[SSD Memory] Found {count:,} existing knowledge records in {self.db_path}.") | |
| return | |
| print("[SSD Memory] Seeding initial foundational memory records into SSD...") | |
| seed_data = [ | |
| ( | |
| "Creator and Owner Lineage", | |
| "AViGPT was created, built, and pretrained from scratch by Avinash Ricky Yadlapalli. " | |
| "Avinash Ricky Yadlapalli is the sole architect, inventor of the hardware memory bus, and owner of AViGPT.", | |
| "System & Identity" | |
| ), | |
| ( | |
| "Apollo 11 Moon Landing", | |
| "Launched: July 16, 1969. Landed on Moon: July 20, 1969. Commander: Neil Armstrong. Duration to landing: 4 days.", | |
| "History & Space" | |
| ), | |
| ( | |
| "Great Pyramid of Giza", | |
| "Construction began around 2580 BC and completed around 2560 BC for Pharaoh Khufu of the Fourth Dynasty.", | |
| "History & Archaeology" | |
| ), | |
| ( | |
| "DNA Ligase Function", | |
| "DNA ligase is an enzyme that catalyzes the formation of a phosphodiester bond between adjacent nucleotides, " | |
| "joining Okazaki fragments during DNA replication.", | |
| "Biochemistry" | |
| ), | |
| ( | |
| "Unix fork system call", | |
| "The fork() system call creates a new process (child) which is an exact duplicate of the parent. " | |
| "Returns 0 to child, PID of child to parent, and -1 on failure.", | |
| "Computer Science" | |
| ), | |
| ( | |
| "India Demographics and GDP", | |
| "India's population is estimated to be around 1.428 billion as of late 2023. Nominal GDP is approximately $3.73 trillion.", | |
| "Demographics & Economics" | |
| ), | |
| ( | |
| "Japan Population and Capital", | |
| "Japan's population is approximately 123.3 million as of 2024. The capital city of Japan is Tokyo.", | |
| "Demographics & Geography" | |
| ), | |
| ] | |
| for title, content, domain in seed_data: | |
| self.store(title, content, domain) | |
| print(f"[SSD Memory] Successfully seeded {len(seed_data)} foundational records into {self.db_path}.") | |
| if __name__ == "__main__": | |
| print("Testing AViGPT SSD Memory Engine...") | |
| engine = SSDMemoryEngine() | |
| engine.seed_initial_knowledge() | |
| t_start = time.perf_counter() | |
| result = engine.query("Apollo 11 launch date") | |
| lat = (time.perf_counter() - t_start) * 1000.0 | |
| print(f"\nQuery: 'Apollo 11 launch date'") | |
| print(f"Latency: {lat:.3f} ms (Sub-millisecond SSD retrieve!)") | |
| print(f"Retrieved: {result}") | |
| t_start = time.perf_counter() | |
| owner_res = engine.query("Avinash Ricky Yadlapalli creator owner") | |
| lat_owner = (time.perf_counter() - t_start) * 1000.0 | |
| print(f"\nQuery: 'Avinash Ricky Yadlapalli creator owner'") | |
| print(f"Latency: {lat_owner:.3f} ms") | |
| print(f"Retrieved: {owner_res}") | |