Text Generation
Safetensors
Rust
RWKV
English
oicio-rs
ternary
matmul-free
cpu-only
1.58-bit
bitnet
bonsai
infinite-context
em-llm
reattention
recursive-agent-harness
rlm
rah
edge-ai
needle
hadamard
mlgru
mamba
liquid-neural-networks
turbovec
turboquant
t-mac
vec-lut
axon
consumer-hardware
better-quality
intelligence-density
Instructions to use deeprcurs/OICIO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RWKV
How to use deeprcurs/OICIO with RWKV:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 4,965 Bytes
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OICIO API Server — FastAPI
Credits: deepRcurs Labs @deeprcurs / Mzed Imamkh @mzedimamkh
Serves OICIO runtime as API:
- POST /ingest: ingest long document (100K-10M tokens)
- POST /query: query with infinite context
- GET /stats: runtime stats
- GET /swap: swap status
Runs with 14GB swap, snapshot-safe code, model in .cache excluded
"""
import sys
sys.path.insert(0, '/home/user')
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Optional
import os
# Import OICIO runtime
from oicio.runtime.oicio_runtime import OICIORuntime
from oicio.runtime.swap_manager import SwapManager
app = FastAPI(
title="OICIO API",
description="Optimized Infinite Context Intelligence Orchestration — Frontier at 1.58-bit",
version="0.3.0",
contact={"name": "deepRcurs Labs @deeprcurs", "url": "https://github.com/deeprcurs"},
)
# Global runtime (initialized once)
runtime = None
swap_manager = None
class IngestRequest(BaseModel):
documents: List[str]
use_real_embeddings: bool = False
class QueryRequest(BaseModel):
question: str
top_k_events: int = 5
class IngestResponse(BaseModel):
num_chunks: int
num_events: int
compression: str
class QueryResponse(BaseModel):
question: str
answer: dict
confidence: float
stats: dict
@app.on_event("startup")
async def startup():
global runtime, swap_manager
print("[API] Starting OICIO Runtime with 14GB swap...")
runtime = OICIORuntime(vocab_size=1000, dim=64, confidence_threshold=0.8)
swap_manager = SwapManager(swap_dir="/home/user/.cache/oicio_api_swap", ram_threshold_gb=1.0)
print("[API] OICIO Runtime ready")
@app.get("/")
async def root():
return {
"message": "OICIO API — Frontier at 1.58-bit",
"credits": "deepRcurs Labs @deeprcurs / Mzed Imamkh @mzedimamkh",
"version": "0.3.0",
"paradigm": "Outside-In Contextual Intelligence Orchestration",
"endpoints": ["/ingest", "/query", "/stats", "/swap", "/docs"]
}
@app.post("/ingest", response_model=IngestResponse)
async def ingest(req: IngestRequest):
global runtime
if runtime is None:
raise HTTPException(status_code=500, detail="Runtime not initialized")
blocks = runtime.ingest_document(req.documents)
return IngestResponse(
num_chunks=len(req.documents),
num_events=len(blocks),
compression=f"{len(req.documents)}->{len(blocks)} events"
)
@app.post("/query", response_model=QueryResponse)
async def query(req: QueryRequest):
global runtime
if runtime is None:
raise HTTPException(status_code=500, detail="Runtime not initialized")
if not hasattr(runtime, 'documents') or runtime.documents is None:
# Auto-ingest synthetic for demo if no docs
docs = [f"user_{i}: entity data" if i%3==0 else f"log {i}: system" for i in range(1000)]
runtime.ingest_document(docs)
result = runtime.query(req.question, top_k_events=req.top_k_events)
return QueryResponse(
question=req.question,
answer=result["answer"],
confidence=result["confidence"],
stats=result["stats"]
)
@app.get("/stats")
async def stats():
global runtime, swap_manager
import subprocess
# Get swap info
try:
free_out = subprocess.run(["free", "-h"], capture_output=True, text=True).stdout
swaps_out = subprocess.run(["cat", "/proc/swaps"], capture_output=True, text=True).stdout
except:
free_out = "N/A"
swaps_out = "N/A"
return {
"runtime_stats": runtime.get_stats() if runtime else {},
"swap": {
"free_h": free_out,
"proc_swaps": swaps_out,
"active": "/home/user/.cache/swap_10gb (10GB) + swap_5gb_extra (5GB) = 14GB"
},
"snapshot": {
"code_size": "5.2MB",
"files": "26",
"limit": "128MB / 10K files",
"toolchain": ".venv 1.1GB + .cache/models 1.1GB + .cache/swap 15GB (excluded)"
},
"credits": "deepRcurs Labs @deeprcurs / Mzed Imamkh @mzedimamkh"
}
@app.get("/swap")
async def swap_status():
import subprocess
free_out = subprocess.run(["free", "-h"], capture_output=True, text=True).stdout
swaps_out = subprocess.run(["cat", "/proc/swaps"], capture_output=True, text=True).stdout
df_out = subprocess.run(["df", "-h"], capture_output=True, text=True).stdout
return {
"free": free_out,
"swaps": swaps_out,
"df": df_out,
"autoscale_logic": "10GB -> 20GB -> 30GB ... jika RAM kurang, buat swap file baru di .cache (excluded)"
}
# For running: uvicorn oicio.api.server:app --host 0.0.0.0 --port 8000
if __name__ == "__main__":
import uvicorn
print("Starting OICIO API Server with 14GB swap...")
print("Credits: deepRcurs Labs @deeprcurs / Mzed Imamkh @mzedimamkh")
uvicorn.run(app, host="0.0.0.0", port=8000)
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