Model: SteelStorage/Aura-Llama-Abliterated Source: Original Platform
license, tags, base_model, model-index
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Aura-llama-3-Abliterated
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 |
Description