metadata
license: apache-2.0
tags:
- merge
- mergekit
base_model:
- failspy/Llama-3-8B-Instruct-abliterated
model-index:
- name: Aura-Llama-Abliterated
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 49.23
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/Aura-Llama-Abliterated
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 72.27
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/Aura-Llama-Abliterated
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 55.71
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/Aura-Llama-Abliterated
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 46.63
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/Aura-Llama-Abliterated
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 69.3
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/Aura-Llama-Abliterated
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 27.6
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TheSkullery/Aura-Llama-Abliterated
name: Open LLM Leaderboard
Exllamav2 quant (exl2 / 6.5 bpw) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
Quant | Model Size | lm_head |
---|---|---|
Aura-llama-3-Abliterated
![Aura-llama-Abliterated Image](https://cdn-uploads.huggingface.co/production/uploads/64545af5ec40bbbd01242ca6/AwLNDVB-GIY7k0wnVV_TX.png)
Now that the cute anime girl has your attention.
UPDATE: Model is now using the abliterated version of meta llama 3 8b
Aura-llama is using the methodology presented by SOLAR for scaling LLMs called depth up-scaling (DUS), which encompasses architectural modifications with continued pretraining. Using the solar paper as a base, I integrated Llama-3 weights into the upscaled layers, and In the future plan to continue training the model.
Aura-llama is a merge of the following models to create a base model to work from:
Abliterated Merged Evals (Has Not Been Finetuned):
Aura-llama-Abliterated
- Avg: ?
- ARC: ?
- HellaSwag: ?
- MMLU: ?
- T-QA: ?
- Winogrande: ?
- GSM8K: ?
Non Abliterated Merged Evals (Has Not Been Finetuned):
Aura-llama-Original
- Avg: 63.13
- ARC: 58.02
- HellaSwag: 77.82
- MMLU: 65.61
- T-QA: 51.94
- Winogrande: 73.40
- GSM8K: 52.01
🧩 Configuration
dtype: bfloat16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 12]
model: failspy/Llama-3-8B-Instruct-abliterated
- sources:
- layer_range: [8, 20]
model: failspy/Llama-3-8B-Instruct-abliterated
- sources:
- layer_range: [16, 28]
model: failspy/Llama-3-8B-Instruct-abliterated
- sources:
- layer_range: [24, 32]
model: failspy/Llama-3-8B-Instruct-abliterated
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 53.46 |
AI2 Reasoning Challenge (25-Shot) | 49.23 |
HellaSwag (10-Shot) | 72.27 |
MMLU (5-Shot) | 55.71 |
TruthfulQA (0-shot) | 46.63 |
Winogrande (5-shot) | 69.30 |
GSM8k (5-shot) | 27.60 |