base_model, library_name, tags, language, license, pipeline_tag

base_model	library_name	tags	language	license	pipeline_tag
ewald1976/Corridor-D-12B ewald1976/Corridor-C-12B	transformers	merge mergekit mistral-nemo 12b creative-writing literary speculative-fiction science-fiction atmospheric synthetic-data roleplay narrative contemplative procedural archival observational-prose restrained-writing synthetic	en	apache-2.0	text-generation

Corridor-D-RevC-12B

Modelcard: ewald1976/Corridor-D-RevC-12B

Description: Corridor-D-RevC-12B s a revision of Corridor-D including Corridor-C, incorporating feedback from deployment. The model maintains the core behavioral traits of its predecessor while addressing known issues in memory usage and response latency.

Architecture: Corridor-D builds on the modular architecture of Corridor-C, with additional layers for sensory processing and motor control. Key improvements include:

1. Memory optimizations: Reorganized data structures to reduce cache misses.
2. Latency reduction: Refactored communication pathways between modules.
3. Noise resilience: Added error-checking routines at critical junctures.

Behavior: The model retains the core behavioral traits of Corridor-C, including:

• Navigation through unfamiliar environments
• Object recognition and manipulation
• Adaptive decision-making based on environmental cues

New behaviors introduced in RevC include:

1. Improved obstacle avoidance using depth perception.
2. Enhanced prioritization of critical tasks under stress conditions.

Deployment Status: Corridor-D is currently in limited deployment, with performance metrics indicating improvement over previous versions. Feedback from early users suggests reduced cognitive load and increased reliability.

Limitations: Known limitations include potential issues with high-precision spatial reasoning and occasional hesitation in ambiguous situations.

Roadmap: Future revisions will focus on integrating recent advances in attention modeling and improving energy efficiency during idle states.

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This is a merge of pre-trained language models created using mergekit.

Configuration

The following YAML configuration was used to produce this model:

# corridor-d-revc-12b.yaml
models:
  - model: ewald1976/Corridor-D-12B
  - model: ewald1976/Corridor-C-12B

merge_method: slerp
base_model: ewald1976/Corridor-D-12B

parameters:
  t:
    - filter: self_attn
      value: 0.25
    - filter: mlp
      value: 0.35
    - value: 0.30

dtype: bfloat16

Settings

• temp: 0.6-0.7
• rep pen: 1.05
• min_p: 0.05
• top_p: 0.90-0.95

This model is provided as-is, without warranty of any kind. The creator of this merge accepts no responsibility for any content generated by the model, including harmful, offensive, misleading, or otherwise objectionable outputs. You use it at your own risk.