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---
license: llama3
library_name: transformers
tags:
- mergekit
- merge
base_model:
- Dampfinchen/Llama-3-8B-Ultra-Instruct
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B-Instruct
model-index:
- name: Llama-3-8B-Ultra-Instruct-SaltSprinkle
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: 61.35
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Dampfinchen/Llama-3-8B-Ultra-Instruct-SaltSprinkle
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: 77.76
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Dampfinchen/Llama-3-8B-Ultra-Instruct-SaltSprinkle
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: 67.88
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Dampfinchen/Llama-3-8B-Ultra-Instruct-SaltSprinkle
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: 52.82
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Dampfinchen/Llama-3-8B-Ultra-Instruct-SaltSprinkle
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: 74.98
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Dampfinchen/Llama-3-8B-Ultra-Instruct-SaltSprinkle
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: 70.89
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Dampfinchen/Llama-3-8B-Ultra-Instruct-SaltSprinkle
name: Open LLM Leaderboard
---
# 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 [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) as a base.
### Models Merged
The following models were included in the merge:
* [Dampfinchen/Llama-3-8B-Ultra-Instruct](https://huggingface.co/Dampfinchen/Llama-3-8B-Ultra-Instruct)
* [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: NousResearch/Meta-Llama-3-8B-Instruct
parameters:
density: 1
weight: 1
- model: Dampfinchen/Llama-3-8B-Ultra-Instruct
parameters:
density: 0.5
weight: 0.2
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
dtype: bfloat16
```
Test of salt sprinkle methode. The goal is to retain all of L3 Instruct's capabilities while adding better RP, RAG, German and story writing capabilities in the form of Ultra Instruct. Model may generate harmful responses, I'm not responsible for what you do with this model.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Dampfinchen__Llama-3-8B-Ultra-Instruct-SaltSprinkle)
| Metric |Value|
|---------------------------------|----:|
|Avg. |67.61|
|AI2 Reasoning Challenge (25-Shot)|61.35|
|HellaSwag (10-Shot) |77.76|
|MMLU (5-Shot) |67.88|
|TruthfulQA (0-shot) |52.82|
|Winogrande (5-shot) |74.98|
|GSM8k (5-shot) |70.89|
|