Engineered for the Next Era of AI Intelligence.
Lightweight, state-of-the-art open foundation models engineered for developers, researchers, and enterprise AI infrastructure. Built with total transparency, 1M context recall, and zero restrictions. Developed by Shakalya International.
QUANTSILICA PRO V1.5 ENGINE32B Active / 70B MoE SwiGLU decoder architecture with dynamic top-2 expert routing and zero latency degradation.
Engineered with an enhanced mixture-of-experts alignment architecture, featuring 1M token needle-in-a-haystack recall, advanced mathematical reasoning, and full open weights. Optimized for enterprise inference on standard GPU clusters.
Every component of the Quantsilica stack is designed for transparency, extreme inference throughput, and enterprise reliability.
Full transparent weight matrices, architectural specifications, and dataset mixture breakdowns. Free commercial usage for global developers and enterprises.
Built-in robust alignment guardrails, governance specifications, low-latency API serving, and zero-data-retention private cloud deployment protocols.
Optimized 8-head GQA projection and FlashAttention-3 CUDA kernels deliver state-of-the-art token throughput on consumer & enterprise GPUs.
Flawless 100% needle-in-a-haystack retrieval performance across 1,048,576 tokens for complex codebase analysis and long document reasoning.
Pre-configured adapter scripts for PyTorch, Unsloth, DeepSpeed, and Axolotl. Fine-tune on custom domain datasets in under 15 minutes.
Zero-friction serving with Ollama, vLLM, Hugging Face Transformers, llama.cpp, and OpenAI-compatible REST endpoints.
Frontier Reasoning & 1M Token Context SOTA
Scientific literature synthesis, mathematical proofs, long-document codebase RAG, complex multi-step logic.
Rigorously evaluated on open evaluation frameworks (lm-evaluation-harness & vLLM) with zero-shot & few-shot standard prompts.
Engineered with universal API compatibility. Run natively in Python, spin up containerized vLLM serving, or execute zero-cost quantized GGUF inference on consumer hardware.
# Install dependencies
# pip install transformers torch accelerate
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "shakalya/quantsilica-1.5-pro"
# Load tokenizer and model with 1M context FlashAttention-3
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
attn_implementation="flash_attention_2"
)
prompt = "Analyze the structural breakdown of quantum silica nanostructures."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
# Generate response
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.2)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Shakalya Research Team • Dr. A. V. Rao, Dr. M. E. Vance et al.
We present the architectural design, pre-training corpus curation, and alignment methodology for the Quantsilica model family, scaling context window retention to 1M tokens with near-zero degradation.
Quantsilica Systems Lab • K. L. Thorne, S. Patel
An investigation into grouped-query attention parameterization for low-vram devices, demonstrating sub-10ms token generation latency on 4GB memory edge accelerators.
AI Alignment & Safety Group • Shakalya International
Comprehensive red-teaming benchmarks, jailbreak resistance metrics, and transparent refusal boundaries across 50,000 adversarial safety prompts.
Benchmarking Group • Shakalya International
Standardized evaluation harness design for testing 128k to 1M token needle retrieval across dense scientific documents and complex multi-file codebases.
Designed for native interoperability with standard machine learning tooling, cloud orchestration, and enterprise development stacks.
Native PyTorch & Hugging Face integration
Direct model weights & Safetensors download
One-line local GGUF execution CLI
High-throughput paged attention server
Pre-built CUDA & ROCm GPU containers
Production auto-scaling helm charts
Local copilot code completion extension
Interactive fine-tuning & evaluation notebooks
Native chat & memory chain abstractions
High-density enterprise document indexing
Native PyTorch & Hugging Face integration
Direct model weights & Safetensors download
One-line local GGUF execution CLI
High-throughput paged attention server
Pre-built CUDA & ROCm GPU containers
Production auto-scaling helm charts
Local copilot code completion extension
Interactive fine-tuning & evaluation notebooks
Native chat & memory chain abstractions
High-density enterprise document indexing
Everything you need to integrate, fine-tune, and scale Quantsilica foundation checkpoints in production.
Learn how to initialize the Quantsilica 32B model, set up tokenizer parameters, and execute your first prompt with FlashAttention-3.
pip install transformers torch accelerate flash-attn --no-build-isolationfrom transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("shakalya/quantsilica-1.5-pro")Across Hugging Face & Ollama
Open-source community
Global AI researchers
Peer-reviewed & arXiv
A deep dive into position embedding scaling, FlashAttention-3 KV-cache optimizations, and tensor quantization techniques used in Quantsilica 1.5-Pro.
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