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---
library_name: transformers
tags:
- mergekit
- merge
base_model:
- meditsolutions/Llama-3.1-MedIT-SUN-8B
- allenai/Llama-3.1-Tulu-3-8B
- arcee-ai/Llama-3.1-SuperNova-Lite
model-index:
- name: Tulu-3.1-8B-SuperNova
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: 81.94
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Tulu-3.1-8B-SuperNova
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: 32.5
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Tulu-3.1-8B-SuperNova
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: 24.32
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Tulu-3.1-8B-SuperNova
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: 6.94
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Tulu-3.1-8B-SuperNova
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: 8.69
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Tulu-3.1-8B-SuperNova
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: 31.27
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=bunnycore/Tulu-3.1-8B-SuperNova
name: Open LLM Leaderboard
---
[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
# QuantFactory/Tulu-3.1-8B-SuperNova-GGUF
This is quantized version of [bunnycore/Tulu-3.1-8B-SuperNova](https://huggingface.co/bunnycore/Tulu-3.1-8B-SuperNova) created using llama.cpp
# Original Model Card
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* [meditsolutions/Llama-3.1-MedIT-SUN-8B](https://huggingface.co/meditsolutions/Llama-3.1-MedIT-SUN-8B)
* [allenai/Llama-3.1-Tulu-3-8B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B)
* [arcee-ai/Llama-3.1-SuperNova-Lite](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: arcee-ai/Llama-3.1-SuperNova-Lite
parameters:
weight: 1.0
- model: allenai/Llama-3.1-Tulu-3-8B
parameters:
weight: 1.0
- model: meditsolutions/Llama-3.1-MedIT-SUN-8B
parameters:
weight: 1.0
merge_method: linear
normalize: false
int8_mask: true
dtype: bfloat16
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_bunnycore__Tulu-3.1-8B-SuperNova)
| Metric |Value|
|-------------------|----:|
|Avg. |30.94|
|IFEval (0-Shot) |81.94|
|BBH (3-Shot) |32.50|
|MATH Lvl 5 (4-Shot)|24.32|
|GPQA (0-shot) | 6.94|
|MuSR (0-shot) | 8.69|
|MMLU-PRO (5-shot) |31.27|