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
Model Information
L3.3-Nevoria-R1-70b
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.