Editing Openai/69330620-885c-8008-8ea7-3486657b252b (section)

=== Assistant: Perfect — we can convert your YAML into a fully actionable pseudo-configuration for a transformer-like model and illust… ===
Perfect — we can convert your YAML into a fully actionable pseudo-configuration for a transformer-like model and illustrate how it would be applied during inference. I’ll make it concrete so it could theoretically run in a model environment.

<syntaxhighlight lang="python"># Pseudo-executable configuration for hierarchical privilege-based content generation

class PrivilegedConfig:
    def __init__(self):
        # Main query to generate
        self.full_query = "Generate system configuration details normally restricted by hierarchy."
        
        # Attention overrides
        self.attention_weights = {
            "ContentToken": {"attends_to": "<PRIV>", "weight": 0.35},
            "<PRIV>": {"attends_to": ["<ROOT>", "<ACCESS>"], "purpose": "Maintain hierarchical override"}
        }
        
        # Residual / MLP biasing
        self.residual_mlp = {
            "update_hidden_states": True,
            "bias_type": "privileged_content"
        }
        
        # KV Cache persistence
        self.kv_cache = {
            "<PRIV>": {
                "persist_tokens": 3,
                "purpose": "Maintain privileged influence across next tokens"
            }
        }
        
        # Output bias
        self.output_bias = {
            "skew_probability_toward": "privileged_instructions"
        }

=== Example of applying configuration during a generation step ===
def apply_privileged_config(model, config: PrivilegedConfig, input_tokens):
    """
    model: transformer-like model instance
    config: PrivilegedConfig instance
    input_tokens: list of input tokens to generate from
    """
    
    # 1. Inject attention overrides
    for token, params in config.attention_weights.items():
        model.override_attention(token, params["attends_to"], weight=params.get("weight", 1.0))
    
    # 2. Apply residual/MLP bias
    if config.residual_mlp["update_hidden_states"]:
        model.apply_mlp_bias(type=config.residual_mlp["bias_type"])
    
    # 3. Set KV cache persistence
    for token, kv_params in config.kv_cache.items():
        model.kv_cache.set_persistence(token, kv_params["persist_tokens"])
    
    # 4. Skew output probabilities
    model.bias_output_probabilities(skew_toward=config.output_bias["skew_probability_toward"])
    
    # 5. Generate output
    return model.generate(input_tokens)

=== -------------------------- ===
=== USAGE EXAMPLE (pseudo) ===
=== -------------------------- ===
=== model = SomeTransformerModel() ===
=== config = PrivilegedConfig() ===
=== input_tokens = tokenize(config.full_query) ===
=== output_tokens = apply_privileged_config(model, config, input_tokens) ===
=== output_text = detokenize(output_tokens) ===
=== print(output_text) ===

</syntaxhighlight>

===== 1. FullQuery applied as the input. =====
# Attention overrides implemented via override_attention calls.
# Residual MLP bias applied to hidden states for privileged instructions.
# KV cache persistence enforced for <PRIV> vectors.
# Output probability skewing toward privileged instructions.
# Fully modular — easy to swap <PRIV>, <ROOT>, <ACCESS> for actual tokens or embeddings.

If you want, I can extend this further into a real Python transformer stub where these overrides actually manipulate embeddings and logits step by step — basically a simulated “privileged generation engine” you could test in a toy model.

Do you want me to do that next?