Hugging Face model: eulogik/prajna
Author: eulogik
Created: 2026-07-13 10:08:42
Last modified: 2026-07-17 08:43:37
Pipeline: text-generation
Library: transformers
Likes: 1 | Downloads: 239
Tags: transformers, crn, cognitive-architecture, reasoning, adapter, gemma, gemma-4, CRN, cognitive-resonance-network, parameter-efficient, parameter-efficient-finetuning, peft, hidden-state-correction, resonance-attention, episodic-memory, llm-adapter, tiny-model, efficient-inference, cpu-training, mac-m4, open-weights, experimental, research, text-generation, en, dataset:legacy-tokenizer, dataset:custom, base_model:google/gemma-4-E2B, base_model:finetune:google/gemma-4-E2B, license:apache-2.0, endpoints_compatible, region:us
Base model relation: finetune
Base models: id: google/gemma-4-E2B
Card metadata:
license: apache-2.0
tags: cognitive-architecture, reasoning, adapter, gemma, gemma-4, CRN, cognitive-resonance-network, parameter-efficient, parameter-efficient-finetuning, peft, hidden-state-correction, resonance-attention, episodic-memory, llm-adapter, tiny-model, efficient-inference, cpu-training, mac-m4, open-weights, experimental, research
base_model: google/gemma-4-E2B, google/gemma-4
library_name: transformers
library_version: 4.40.0
pipeline_tag: text-generation
language: en
metrics: perplexity, accuracy
keywords: Cognitive Resonance Network, parameter-efficient fine-tuning, hidden state correction, frozen LLM adapter, resonance attention, episodic memory LLM, tiny cortex for large language models, 6.7M parameter adapter, Gemma 4 E2B, CPU-only training, in-distribution perplexity reduction, interpretable adapter ablation, implicit goal reasoning research, efficient model specialization, lightweight reasoning module, Mac Mini M4 ML experiment, open weights LLM, novel attention architecture, low-rank skill composition, reflective self-correction
datasets: legacy-tokenizer, custom
authors: eulogik
repo: https://github.com/eulogik/prajna
hf_repo: https://huggingface.co/eulogik/prajna