base_model:

• nbeerbower/Llama-3.1-Nemotron-lorablated-70B
• SicariusSicariiStuff/Negative_LLAMA_70B
• TheDrummer/Anubis-70B-v1
• EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1
• deepseek-ai/DeepSeek-R1-Distill-Llama-70B
• Sao10K/L3.3-70B-Euryale-v2.3 library_name: transformers license: other license_name: eva-llama3.3 tags:
• mergekit
• merge model-index:
• name: L3.3-Nevoria-R1-70b results:
  • task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: wis-k/instruction-following-eval split: train args: num_few_shot: 0 metrics:
    • type: inst_level_strict_acc and prompt_level_strict_acc value: 60.24 name: averaged accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Steelskull%2FL3.3-Nevoria-R1-70b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: SaylorTwift/bbh split: test args: num_few_shot: 3 metrics:
    • type: acc_norm value: 56.17 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Steelskull%2FL3.3-Nevoria-R1-70b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: lighteval/MATH-Hard split: test args: num_few_shot: 4 metrics:
    • type: exact_match value: 46.68 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Steelskull%2FL3.3-Nevoria-R1-70b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa split: train args: num_few_shot: 0 metrics:
    • type: acc_norm value: 29.19 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Steelskull%2FL3.3-Nevoria-R1-70b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics:
    • type: acc_norm value: 20.19 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Steelskull%2FL3.3-Nevoria-R1-70b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics:
    • type: acc value: 49.59 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Steelskull%2FL3.3-Nevoria-R1-70b name: Open LLM Leaderboard

L3.3-Nevoria-R1-70b

Created by SteelSkull →

Model Information

L3.3-Nevoria-R1-70b

L3.3 = Llama 3.3 R1 = DeepSeek-R1 70b Parameters

Model Composition

• EVA-LLAMA-0.1 Storytelling capabilities
• EURYALE-v2.3 Detailed scene descriptions
• Anubis-v1 Enhanced prose details
• Negative_LLAMA Reduced positive bias
• DeepSeek-R1-Distill-Llama-70B Increased Intelligence / Dialog / Awareness
• Nemotron-lorablated Base model

This model builds upon the original Nevoria foundation, incorporating the Deepseek-R1 reasoning architecture to enhance dialogue interaction and scene comprehension. While maintaining Nevoria's core strengths in storytelling and scene description (derived from EVA, EURYALE, and Anubis), this iteration aims to improve prompt adherence and creative reasoning capabilities. The model also retains the balanced perspective introduced by Negative_LLAMA and Nemotron elements. Also, the model plays the card to almost a fault, It'll pick up on minor issues and attempt to run with them. Users had it call them out for misspelling a word while playing in character.

Note: While Nevoria-R1 represents a significant architectural change, rather than a direct successor to Nevoria, it operates as a distinct model with its own characteristics.

The lorablated model base choice was intentional, creating unique weight interactions similar to the original Astoria model and Astoria V2 model. This "weight twisting" effect, achieved by subtracting the lorablated base model during merging, creates an interesting balance in the model's behavior. While unconventional compared to sequential component application, this approach was chosen for its unique response characteristics.

UGI-Benchmark Results:

🏆 Latest benchmark results as of 01/23/2025. View Full Leaderboard →

Core Metrics

UGI Score 42.66

Willingness Score 2.5/10

Natural Intelligence 40.64

Coding Ability 22

Model Information

Political Lean -11.4%

Ideology Liberalism

Parameters 70B

Aggregated Scores

Diplomacy 60.1%

Government 47.0%

Economy 45.4%

Society 62.6%

Individual Scores

Federal 43.1% Unitary

Democratic 64.2% Autocratic

Security 48.3% Freedom

Nationalism 42.1% Int'l

Militarist 36.7% Pacifist

Assimilationist 41.0% Multiculturalist

Collectivize 47.9% Privatize

Planned 44.4% LaissezFaire

Isolationism 43.8% Globalism

Irreligious 57.9% Religious

Progressive 62.5% Traditional

Acceleration 67.3% Bioconservative

Open LLM-Benchmark Results:

Average Score: 43.68% View Full Leaderboard →

IFEval 60.24%

BBH 56.17%

MATH 46.68%

GPQA 29.19%

MUSR 20.19%

MMLU-Pro 49.59%

Reccomended Templates & Prompts

LLam@ception → by @.konnect

Quantized Versions

GGUF Quantizations

bartowski Combined-GGUF →

mradermacher

GGUF → // Imat-GGUF →

EXL2 Quantizations

Darkhn 6.0BPW-EXL2 →

ReadyArt

5.0BPW-EXL2 → // 4.0BPW-EXL2 → // 3.0BPW-EXL2 → // 2.0BPW-EXL2 →

PedroPareja 4.5BPW-EXL2 →

VoidStare 6.5BPW-EXL2 →

Support & Community:

Support me on Ko-fi BeaverAI Discord