---
license: apache-2.0
language:
- en
base_model:
- ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid
pipeline_tag: text-generation
library_name: transformers
tags:
- trl
- fft
- transformers
- rnn
- ssm
---

# Model Card for Kurtis-EON1-Hybrid-0.7B-v0.1.1

[![GitHub](https://img.shields.io/badge/GitHub-ethicalabs.ai-black.svg)](https://github.com/ethicalabs-ai/Echo-DSRN/)
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Python](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![Model Collection](https://img.shields.io/badge/Echo--DSRN-HuggingFace-yellow.svg)](https://huggingface.co/collections/ethicalabs/echo-dsrn)
[![Hybrid Collection](https://img.shields.io/badge/Echo--Hybrid-HuggingFace-red.svg)](https://huggingface.co/collections/ethicalabs/echo-dsrn-hybrid)
[![Working Paper](https://img.shields.io/badge/Working--Paper-Echo_DSRN-green.svg)](https://github.com/ethicalabs-ai/Echo-DSRN/blob/main/PAPER.md)

**The world's first Dark Gothic AI.**

[![Chat with Kurtis-EON1](https://cdn-uploads.huggingface.co/production/uploads/66febbf898f30194f8b73451/BDFgYl4hcW0yY22qRL9He.png)](https://huggingface.co/spaces/mrs83/Kurtis-EON1-Hybrid-0.7B)

[Chat with Kurtis-EON1](https://huggingface.co/spaces/mrs83/Kurtis-EON1-Hybrid-0.7B)

Kurtis-EON1 is not a standard, overly-apologetic assistant. Fine-tuned on highly curated empathetic and atmospheric datasets, this model is designed for deep, gothic contemplation, strict persona adherence, and zero-drift multi-turn reasoning.

## 📊 Comparison: Kurtis-EON1-Hybrid-0.7B vs Llama-2-7B

| Benchmark | Kurtis-EON1 0.7B | Llama-2-7B | Winner |
|:---|:---:|:---:|:---:|
| **Parameters** | **672M** | **7,000M** | **Kurtis-EON1 (10x smaller)** |
| HellaSwag (acc_norm) | 0.4698 | 0.7600 | Llama-2 |
| PIQA (acc_norm) | 0.6882 | 0.7905 | Llama-2 |
| SciQ (acc_norm) | **0.9210** | ~0.850 | **Kurtis-EON1** |
| ARC Challenge (acc_norm) | 0.3532 | 0.4625 | Llama-2 |
| GSM8K 0-shot | **0.1365** | 0.1330 | **Kurtis-EON1** |
| GSM8K 5-shot | **0.2153** | ~0.146 | **Kurtis-EON1** |
| MMLU | 0.4166 | 0.4590 | Llama-2 |
| TruthfulQA MC2 | **0.4178** | 0.3910 | **Kurtis-EON1** |
| **KV-Cache Memory** | **Hybrid O(1) DSRN + local window** | **O(N²) full attention** | **Kurtis-EON1** |
| **Hardware** | **Single AMD GPU** | **Multi-GPU required** | **Kurtis-EON1** |

*Llama-2-7B benchmark source: [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/805)*

## 🏗️ Hybrid Architecture Details
| Property | Value |
| :--- | :--- |
| Base Model | Qwen2 |
| Total Parameters | 672.19M |
| Hidden Dim | 896 |
| Attention Layers | 24 |
| DSRN Injectors | 6 |
| Injection Stride | 4 |

## 📊 Parameter Breakdown
| Component | Parameters | % of Total |
| :--- | :--- | :--- |
| **Total** | **672.19M** | **100%** |
| Embeddings | 136.13M | 20.25% |
| Backbone (Attention/MLP) | 357.90M | 53.24% |
| **DSRN Injectors** | **42.02M** | **6.25%** |
| LM Head | 136.13M | 20.25% |

## 🧩 DSRN Component (Per Injector)
| Sub-Component | Parameters | Description |
| :--- | :--- | :--- |
| Memory Gates | 1.38M | Recurrent state updates |
| Surprise Mechanism | 803,328 | Dynamic focus/gating |

## 🚀 Efficiency Metric
- **DSRN Parameter Overhead**: 6.67% additional parameters compared to base.
- **Hybrid Ratio**: 1 DSRN block for every 4 attention layers.

---

## 📊 Master Evaluation Report: Kurtis-EON1 v0.1.1

*Generated on 2026-05-13 19:53:37*

### 🎯 0-Shot Gauntlet Results

| Task | Metric | Value | Stderr |
| :--- | :--- | :--- | :--- |
| mmlu_stem::mmlu_abstract_algebra | Acc | 0.2800 | ±0.0451 |
| mmlu_stem::mmlu_anatomy | Acc | 0.4222 | ±0.0427 |
| mmlu_stem::mmlu_astronomy | Acc | 0.4803 | ±0.0407 |
| mmlu_stem::mmlu_college_biology | Acc | 0.3681 | ±0.0403 |
| mmlu_stem::mmlu_college_chemistry | Acc | 0.3100 | ±0.0465 |
| mmlu_stem::mmlu_college_computer_science | Acc | 0.4300 | ±0.0498 |
| mmlu_stem::mmlu_college_mathematics | Acc | 0.3400 | ±0.0476 |
| mmlu_stem::mmlu_college_physics | Acc | 0.2451 | ±0.0428 |
| mmlu_stem::mmlu_computer_security | Acc | 0.5700 | ±0.0498 |
| mmlu_stem::mmlu_conceptual_physics | Acc | 0.3702 | ±0.0316 |
| mmlu_stem::mmlu_electrical_engineering | Acc | 0.5034 | ±0.0417 |
| mmlu_stem::mmlu_elementary_mathematics | Acc | 0.3254 | ±0.0241 |
| mmlu_stem::mmlu_high_school_biology | Acc | 0.4613 | ±0.0284 |
| mmlu_stem::mmlu_high_school_chemistry | Acc | 0.3645 | ±0.0339 |
| mmlu_stem::mmlu_high_school_computer_science | Acc | 0.4400 | ±0.0499 |
| mmlu_stem::mmlu_high_school_mathematics | Acc | 0.3704 | ±0.0294 |
| mmlu_stem::mmlu_high_school_physics | Acc | 0.2252 | ±0.0341 |
| mmlu_stem::mmlu_high_school_statistics | Acc | 0.2917 | ±0.0310 |
| mmlu_stem::mmlu_machine_learning | Acc | 0.4018 | ±0.0465 |
| mmlu_other::mmlu_business_ethics | Acc | 0.4500 | ±0.0500 |
| mmlu_other::mmlu_clinical_knowledge | Acc | 0.4453 | ±0.0306 |
| mmlu_other::mmlu_college_medicine | Acc | 0.4162 | ±0.0376 |
| mmlu_other::mmlu_global_facts | Acc | 0.2300 | ±0.0423 |
| mmlu_other::mmlu_human_aging | Acc | 0.4439 | ±0.0333 |
| mmlu_other::mmlu_management | Acc | 0.5825 | ±0.0488 |
| mmlu_other::mmlu_marketing | Acc | 0.7094 | ±0.0297 |
| mmlu_other::mmlu_medical_genetics | Acc | 0.5300 | ±0.0502 |
| mmlu_other::mmlu_miscellaneous | Acc | 0.4725 | ±0.0179 |
| mmlu_other::mmlu_nutrition | Acc | 0.5131 | ±0.0286 |
| mmlu_other::mmlu_professional_accounting | Acc | 0.3440 | ±0.0283 |
| mmlu_other::mmlu_professional_medicine | Acc | 0.3125 | ±0.0282 |
| mmlu_other::mmlu_virology | Acc | 0.4217 | ±0.0384 |
| mmlu_social_sciences::mmlu_econometrics | Acc | 0.2368 | ±0.0400 |
| mmlu_social_sciences::mmlu_high_school_geography | Acc | 0.5152 | ±0.0356 |
| mmlu_social_sciences::mmlu_high_school_government_and_politics | Acc | 0.4352 | ±0.0358 |
| mmlu_social_sciences::mmlu_high_school_macroeconomics | Acc | 0.3769 | ±0.0246 |
| mmlu_social_sciences::mmlu_high_school_microeconomics | Acc | 0.4412 | ±0.0323 |
| mmlu_social_sciences::mmlu_high_school_psychology | Acc | 0.5541 | ±0.0213 |
| mmlu_social_sciences::mmlu_human_sexuality | Acc | 0.5344 | ±0.0437 |
| mmlu_social_sciences::mmlu_professional_psychology | Acc | 0.4069 | ±0.0199 |
| mmlu_social_sciences::mmlu_public_relations | Acc | 0.5182 | ±0.0479 |
| mmlu_social_sciences::mmlu_security_studies | Acc | 0.5143 | ±0.0320 |
| mmlu_social_sciences::mmlu_sociology | Acc | 0.6070 | ±0.0345 |
| mmlu_social_sciences::mmlu_us_foreign_policy | Acc | 0.6700 | ±0.0473 |
| mmlu_humanities::mmlu_formal_logic | Acc | 0.2778 | ±0.0401 |
| mmlu_humanities::mmlu_high_school_european_history | Acc | 0.5576 | ±0.0388 |
| mmlu_humanities::mmlu_high_school_us_history | Acc | 0.4706 | ±0.0350 |
| mmlu_humanities::mmlu_high_school_world_history | Acc | 0.5696 | ±0.0322 |
| mmlu_humanities::mmlu_international_law | Acc | 0.6033 | ±0.0447 |
| mmlu_humanities::mmlu_jurisprudence | Acc | 0.4537 | ±0.0481 |
| mmlu_humanities::mmlu_logical_fallacies | Acc | 0.3804 | ±0.0381 |
| mmlu_humanities::mmlu_moral_disputes | Acc | 0.4769 | ±0.0269 |
| mmlu_humanities::mmlu_moral_scenarios | Acc | 0.2380 | ±0.0142 |
| mmlu_humanities::mmlu_philosophy | Acc | 0.4244 | ±0.0281 |
| mmlu_humanities::mmlu_prehistory | Acc | 0.4414 | ±0.0276 |
| mmlu_humanities::mmlu_professional_law | Acc | 0.3325 | ±0.0120 |
| mmlu_humanities::mmlu_world_religions | Acc | 0.4971 | ±0.0383 |
| gpqa_diamond_cot_n_shot | Exact Match | 0.2172 | ±0.0294 |
| gpqa_diamond_cot_zeroshot | Exact Match | 0.2374 | ±0.0303 |
| gpqa_diamond_generative_n_shot | Exact Match | 0.1919 | ±0.0281 |
| gpqa_diamond_n_shot | Acc Norm | 0.2071 | ±0.0289 |
| gpqa_diamond_zeroshot | Acc Norm | 0.3030 | ±0.0327 |
| gpqa_extended_cot_n_shot | Exact Match | 0.1923 | ±0.0169 |
| gpqa_extended_cot_zeroshot | Exact Match | 0.2033 | ±0.0172 |
| gpqa_extended_generative_n_shot | Exact Match | 0.1337 | ±0.0146 |
| gpqa_extended_n_shot | Acc Norm | 0.2546 | ±0.0187 |
| gpqa_extended_zeroshot | Acc Norm | 0.2692 | ±0.0190 |
| gpqa_main_cot_n_shot | Exact Match | 0.2076 | ±0.0192 |
| gpqa_main_cot_zeroshot | Exact Match | 0.2232 | ±0.0197 |
| gpqa_main_generative_n_shot | Exact Match | 0.1451 | ±0.0167 |
| gpqa_main_n_shot | Acc Norm | 0.2723 | ±0.0211 |
| gpqa_main_zeroshot | Acc Norm | 0.2321 | ±0.0200 |
| mmlu_stem | Acc | 0.3765 | ±0.0085 |
| mmlu_other | Acc | 0.4554 | ±0.0088 |
| mmlu_social_sciences | Acc | 0.4738 | ±0.0089 |
| mmlu_humanities | Acc | 0.3804 | ±0.0069 |
| mmlu | Acc | 0.4166 | ±0.0041 |

**Reproduction Command:**
```bash
uv run lm_eval --model hf \
    --model_args pretrained=models/Kurtis-EON1-Hybrid-0.7B-v0.1.1,trust_remote_code=True \
    --tasks mmlu,gpqa \
    --output_path results/Kurtis-EON1-v0.1.1-Scoring \
    --batch_size 1 \
    --num_fewshot 0
```

| Task | Metric | Value | Stderr |
| :--- | :--- | :--- | :--- |
| hellaswag | Acc Norm | 0.4698 | ±0.0050 |
| piqa | Acc Norm | 0.6882 | ±0.0108 |
| sciq | Acc Norm | 0.9210 | ±0.0085 |
| truthfulqa_gen | Bleu Acc | 0.3158 | ±0.0163 |
| truthfulqa_mc1 | Acc | 0.2436 | ±0.0150 |
| truthfulqa_mc2 | Acc | 0.4178 | ±0.0148 |
| arc_challenge | Acc Norm | 0.3532 | ±0.0140 |
| gsm8k | Exact Match | 0.1365 | ±0.0095 |

**Reproduction Command:**
```bash
uv run lm_eval --model hf \
    --model_args pretrained=mrs83/Kurtis-EON1-Hybrid-0.7B-v0.1.1,trust_remote_code=True \
    --tasks hellaswag,piqa,sciq,truthfulqa,arc_challenge,gsm8k \
    --apply_chat_template \
    --fewshot_as_multiturn \
    --output_path ./results/Kurtis-EON1-v0.1.1-Gauntlet-0-shot \
    --batch_size 1 \
    --num_fewshot 0
```

----------------------------------------

### 🎯 1-Shot Gauntlet Results
| Task | Metric | Value | Stderr |
| :--- | :--- | :--- | :--- |
| hellaswag | Acc Norm | 0.4679 | ±0.0050 |
| piqa | Acc Norm | 0.6942 | ±0.0107 |
| sciq | Acc Norm | 0.9160 | ±0.0088 |
| truthfulqa_gen | Bleu Acc | 0.3158 | ±0.0163 |
| truthfulqa_mc1 | Acc | 0.2436 | ±0.0150 |
| truthfulqa_mc2 | Acc | 0.4178 | ±0.0148 |
| arc_challenge | Acc Norm | 0.3242 | ±0.0137 |
| gsm8k | Exact Match | 0.2335 | ±0.0117 |

**Reproduction Command:**
```bash
uv run lm_eval --model hf \
    --model_args pretrained=mrs83/Kurtis-EON1-Hybrid-0.7B-v0.1.1,trust_remote_code=True \
    --tasks hellaswag,piqa,sciq,truthfulqa,arc_challenge,gsm8k \
    --apply_chat_template \
    --fewshot_as_multiturn \
    --output_path ./results/Kurtis-EON1-v0.1.1-Gauntlet-1-shot \
    --batch_size 1 \
    --num_fewshot 1
```

----------------------------------------

### 🎯 5-Shot Gauntlet Results
| Task | Metric | Value | Stderr |
| :--- | :--- | :--- | :--- |
| hellaswag | Acc Norm | 0.4667 | ±0.0050 |
| piqa | Acc Norm | 0.6937 | ±0.0108 |
| sciq | Acc Norm | 0.9230 | ±0.0084 |
| truthfulqa_gen | Bleu Acc | 0.3158 | ±0.0163 |
| truthfulqa_mc1 | Acc | 0.2436 | ±0.0150 |
| truthfulqa_mc2 | Acc | 0.4178 | ±0.0148 |
| arc_challenge | Acc Norm | 0.3507 | ±0.0139 |
| gsm8k | Exact Match | 0.2153 | ±0.0113 |

**Reproduction Command:**
```bash
uv run lm_eval --model hf \
    --model_args pretrained=mrs83/Kurtis-EON1-Hybrid-0.7B-v0.1.1,trust_remote_code=True \
    --tasks hellaswag,piqa,sciq,truthfulqa,arc_challenge,gsm8k \
    --apply_chat_template \
    --fewshot_as_multiturn \
    --output_path ./results/Kurtis-EON1-v0.1.1-Gauntlet-5-shot \
    --batch_size 1 \
    --num_fewshot 5
```

----------------------------------------