metadata
library_name: transformers
base_model:
- mlabonne/NeuralDaredevil-8B-abliterated
- grimjim/Llama-3.1-SuperNova-Lite-lorabilterated-8B
model-index:
- name: NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 79.99
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 30.76
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 10.27
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 4.14
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated
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: 9.47
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated
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: 30.37
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated
name: Open LLM Leaderboard
tags:
- merge
- mergekit
- lazymergekit
- mlabonne/NeuralDaredevil-8B-abliterated
- grimjim/Llama-3.1-SuperNova-Lite-lorabilterated-8B
license: llama3.1
pipeline_tag: text-generation
NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated
NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated is a merge of the following models using LazyMergekit:
Quantised versions of this model are available in GGUF format from here Or use the following direct links:
open-llm-leaderboard results
Average | IFEval | BBH | MATH Lvl 5 | GPQA | MUSR | MMLU-PRO | |
---|---|---|---|---|---|---|---|
27.5 | 79.99 | 30.76 | 10.27 | 4.14 | 9.47 | 30.37 | 🤗 Open LLM Leaderboard |
🧩 Configuration
models:
- model: NousResearch/Meta-Llama-3.1-8B-Instruct
- model: mlabonne/NeuralDaredevil-8B-abliterated
parameters:
density: 0.53
weight: 0.55
- model: grimjim/Llama-3.1-SuperNova-Lite-lorabilterated-8B
parameters:
density: 0.53
weight: 0.45
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3.1-8B-Instruct
parameters:
int8_mask: true
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-ablorabliterated"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])