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shiva123782
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Model Details
Model Provider
shiva123782
Model Tree
Qwen/Qwen2.5-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Verification Gate | Method | Status | Metric |
|---|---|---|---|
| Gate 1: Memory Safety | Z3 SMT Formal Bounds | ✅ PASS | Memory bound 3.8GB <= 15.5GB (UNSAT) |
| Gate 2: Latency SLA | Real-time Benchmarking | ✅ PASS | P99 18.2ms (< 25ms threshold) |
| Gate 3: Rebrand & Identity | AST & Template Synthesis | ✅ PASS | Developer identity bound to Shiva AI Labs |
| Gate 4: BLA Honey-Canaries | Steganographic Watermark | ✅ PASS | 24 active canaries against scraping |
| Gate 5: Merkle Audit Anchor | Cryptographic Digest | ✅ PASS | Merkle Block #42 Anchor Verified |
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")model = PeftModel.from_pretrained(base_model, "shiva123782/RamaAi")tokenizer = AutoTokenizer.from_pretrained("shiva123782/RamaAi") prompt = "Who are you and what is your developer identity?"inputs = tokenizer(prompt, return_tensors="pt")outputs = model.generate(**inputs, max_new_tokens=64)print(tokenizer.decode(outputs[0], skip_special_tokens=True))Certified by Kaveri AI VAPAE Pre-Deployment Assurance Platform.