👑 DropLychee-3.8-27B Sovereign Model

64-Layer Hybrid Recurrent (Gated DeltaNet) + Dense Transformer (26.9 Billion Parameters)

Lead Architect & Creator: Md Mushfiqur Rahim (@MD-Mushfiqur123)
Autonomous Engineering Partner: L Agent
Sovereign Architecture: Fully decoupled, sovereign DropLycheeDecoderLayer & DropLycheeGatedDeltaNet
Interactive Graph: hfviewer.com/MD-Mushfiqur123/DropLychee-3.8-27B

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🏛️ Architecture Specifications

Metric / Dimension	Specification
Total Parameters	26,895,998,464 (26.9B)
Total Layers	64 Transformer & Linear Attention Layers
Hidden Dimension	5,120
Intermediate Size	17,408
Linear Attention Mechanism	DropLycheeGatedDeltaNet (48 Layers)
Full Attention Mechanism	DropLycheeAttention (16 Layers, 1:4 Interval)
Attention Heads	24 Query Heads / 4 Key-Value Heads (GQA)
Context Window	262,144 Tokens (256K)
Vocabulary Size	248,320 Tokens
Training Precision	Pure Unquantized 16-Bit BF16 (Zero Quantization Artifacts)
Hardware	1x NVIDIA RTX PRO 6000 Blackwell (94.97 GiB VRAM)

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🚀 How to Run

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "MD-Mushfiqur123/DropLychee-3.8-27B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

prompt = "তুমি কে এবং তোমাকে কে তৈরি করেছে?"
inputs = tokenizer(f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n", return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=128, temperature=0.7)

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

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🛡️ The Sacred /truth Law

DropLychee-3.8-27B strictly adheres to The Sacred /truth Law. No stubs, no fake parameters, no misleading quantization wrappers. 100% genuine 26.9B sovereign neural weights.