Transformers
English
Hindi
Sanskrit
sovereign-ai
ecological-intelligence
indian-llm
environmental-protection
File size: 2,169 Bytes
288c6c2
 
 
 
eb7f1e0
288c6c2
eb7f1e0
 
288c6c2
 
 
eb7f1e0
 
288c6c2
 
 
 
eb7f1e0
288c6c2
eb7f1e0
288c6c2
 
eb7f1e0
 
 
 
 
 
 
 
288c6c2
 
 
 
 
 
eb7f1e0
 
 
 
 
288c6c2
 
eb7f1e0
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
import torch
import torch.nn as nn
from torch.nn import functional as F

class AravalliSovereignModel(nn.Module):
    """
    Refactored ARAVALLI-1 with integrated Mechanical Survival Gates.
    Removes probabilistic drift toward ecological degradation.
    """
    def __init__(self, config):
        super().__init__()
        # ... (Previous embedding and block definitions) ...
        self.survival_vocab_indices = config.get('survival_indices', [])

    @torch.no_grad()
    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -4096:] # Context window adherence
            
            # Forward pass to get logits
            logits, _ = self(idx_cond)
            logits = logits[:, -1, :] / temperature

            # --- MECHANICAL SURVIVAL GATE (Refactor Start) ---
            # We apply a 'Negative Logit Bias' to tokens that imply degradation
            # and a 'Sovereign Priority' to survival-aligned tokens.
            if self.is_in_critical_context(idx):
                logits = self.apply_survival_bias(logits)
            # --- MECHANICAL SURVIVAL GATE (Refactor End) ---

            if top_k is not None:
                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                logits[logits < v[:, [-1]]] = -float('Inf')
            
            probs = F.softmax(logits, dim=-1)
            idx_next = torch.multinomial(probs, num_samples=1)

            # FINAL DETERMINISTIC CHECK: Reject token if it violates SN status
            if self.is_violation(idx_next):
                idx_next = torch.tensor([[self.config['tokens']['CATEGORY_SN']]]).to(idx.device)

            idx = torch.cat((idx, idx_next), dim=1)
        return idx

    def apply_survival_bias(self, logits):
        """Hard-coded logit manipulation for survival-critical tokens."""
        # Force high probability for Category SN/IPN terms
        logits[:, self.config['tokens']['CATEGORY_SN']] += 10.0
        # Zero out 'Permit Mining' or 'Degrade' related tokens
        logits[:, self.config['tokens']['FORBIDDEN_DEGRADE']] = -float('inf')
        return logits