Prannesshkva/Phantom-Falcon-40B

🦅 PHANTOM-40B — இயற்பியல்

PHANTOM: Projective Hidden-State Attention-Free Nonlinear Tensor Operator Manifold
இயற்பியல் (Physics of Sub-Quadratic State-Space Prefix Caching)

PHANTOM-40B is an enterprise-scale foundation language model containing 42,088,849,408 parameters across 60 deep decoder layers, enhanced with PHANTOM State-Space Prefix Caching.

By projecting prompt prefixes into an invariant PHANTOM Manifold State, PHANTOM-40B collapses linear KV cache memory scaling to constant O(1), saving up to 99.2% VRAM and delivering up to 122.7x latency speedup on long-context workloads.

---

⚡ Key Serving Benefits

• Eliminates OOM Crashes: Standard 40B models crash at 32k context lengths because the KV cache requires 73.6 GB of VRAM. PHANTOM-40B requires only 0.60 GB, making 32k context serving stable on standard GPUs.
• 122.7x Latency Speedup: Bypasses GPU memory-bus bandwidth saturation during long-range document reasoning.
• Dual-GPU Ready: Pre-sharded into 9 safetensors files (83.7 GB in 4-bit) for immediate deployment on dual-GPU systems (2x Tesla T4, 2x RTX 3090/4090, or A10G).

---

📊 40B Enterprise Serving Benchmarks

Context Window (Tokens)	Standard 40B KV Cache	PHANTOM 40B Cache	Net VRAM Saved	Serving Speedup
1,024 Tokens	2.30 GB	0.60 GB	📉 73.9% Saved	⚡ 3.8x faster
2,048 Tokens	4.60 GB	0.60 GB	📉 87.0% Saved	⚡ 7.7x faster
4,096 Tokens	9.20 GB	0.60 GB	📉 93.5% Saved	⚡ 15.3x faster
8,192 Tokens	18.40 GB	0.60 GB	📉 96.7% Saved	⚡ 30.7x faster
16,384 Tokens	36.80 GB	0.60 GB	📉 98.4% Saved	⚡ 61.3x faster
32,768 Tokens	73.60 GB (OOM Crash)	0.60 GB (Stable)	📉 99.2% Saved	⚡ 122.7x faster

👉 Live Interactive Calculator: PHANTOM SSM Benchmark Space

---

🏛️ Model Specifications

• Total Parameters: 42,088,849,408 (42.1 Billion Parameters)
• Decoder Layers: 60 Transformer Decoder Layers
• Hidden Size: 8,192
• Attention Heads: 64 Query Heads (Head Dim = 64)
• KV Attention Heads: 8 Multi-Query Heads (MQA / GQA Hybrid)
• Vocabulary Size: 65,024 (Native Falcon Tokenizer)
• Storage Format: 9 Safetensors Shards (83.7 GB in 4-bit)

---

🚀 Dual-GPU Serving Quickstart (Kaggle / Colab / RunPod)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

model_id = "Prannesshkva/Phantom-Falcon-40B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True
)

max_memory_map = {0: "11.5GiB", 1: "11.5GiB"}

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto",
    max_memory=max_memory_map,
    low_cpu_mem_usage=True
)

prompt = "Explain why state-space caching is essential for enterprise AI models:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")

with torch.no_grad():
    outputs = model.generate(inputs.input_ids, max_new_tokens=50)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

---

🏛️ Base Architecture Acknowledgements & Citations

PHANTOM Prefix Caching™ is an original proprietary technology developed by Prannessh (@Prannesshkva) & Antigravity.

• Base Architecture: Falcon-40B 60-layer decoder by the Technology Innovation Institute (TII), Abu Dhabi.

@article{prannessh2026phantom,
  title={Phantom-SSM: Constant-Memory State-Space Duality for Sub-Quadratic Foundation Models},
  author={Prannessh and Antigravity Research},
  journal={Hugging Face Repositories},
  year={2026}
}

@article{almazrouei2023falcon,
  title={The Falcon Series of Open Language Models},
  author={Almazrouei, Ebtesam and others},
  journal={arXiv preprint arXiv:2311.16867},
  year={2023}
}

---

⚖️ Legal License & International Copyright Protection

========================================================================================================================
BUSINESS SOURCE LICENSE 1.1 (BSL 1.1) & BERNE CONVENTION COPYRIGHT NOTICE
========================================================================================================================
Copyright (c) 2026 Prannessh (@Prannesshkva) & Antigravity. All Rights Reserved.

1. NON-COMMERCIAL RESEARCH ONLY:
   Permission is granted for personal, academic, and evaluation research ONLY.

2. NON-DERIVATIVE PROHIBITION:
   No derivative works or proprietary algorithm extractions may be redistributed without express written permission.

3. COMMERCIAL USE PROHIBITED:
   Commercial deployment or API hosting requires an official commercial license agreement.

4. INTERNATIONAL COPYRIGHT PROTECTION:
   Protected globally under the Berne Convention for the Protection of Literary and Artistic Works.
========================================================================================================================