license: llama3
library_name: transformers
tags:
- mergekit
- merge
base_model:
- Undi95/Meta-Llama-3-8B-Instruct-hf
model-index:
- name: Llama-3-8B-Ultra-Instruct
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: 64.59
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=elinas/Llama-3-8B-Ultra-Instruct
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: 81.63
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=elinas/Llama-3-8B-Ultra-Instruct
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: 68.32
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=elinas/Llama-3-8B-Ultra-Instruct
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: 52.8
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=elinas/Llama-3-8B-Ultra-Instruct
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: 76.95
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=elinas/Llama-3-8B-Ultra-Instruct
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: 70.36
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=elinas/Llama-3-8B-Ultra-Instruct
name: Open LLM Leaderboard
Llama-3-8B-Ultra-Instruct
This is a merge of pre-trained language models created using mergekit.
Hello everyone this is Dampf, creator of the Destroyer series!
looks around Oh, now I'm on Elinas' HF account. As you can see, I'm quite the traveler!
This time, I'm introducing you to 8B-Ultra-Instruct. It is a small general purpose model that combines the most powerful instruct models with enticing roleplaying models. It will introduce better RAG capabilities in the form of Bagel to Llama 3 8B Instruct as well as German multilanguage, higher general intelligence and vision support. A model focused on Biology adds knowledge in the medical field.
As for roleplay, it features two of the hottest models right now. Those are known to be high quality and for being uncensored. So this model might put out harmful responses. We are not responsible for what you do with this model and please take everything the model says with a huge grain of salt. Lastly, you might notice I'm conversative with the weight values in the final merge. This is because I believe L8B Instruct is a very dense model that's already great and doesn't need a lot more data. So instead of reaching the weight value of 1 in a ties merge, I'm only using a total of 0,65. This is to preserve Llama Instruct's intelligence and knowledge, while adding a little bit of the aforementioned models as salt in the soup.
A huge thank you for all the creators of the datasets. Those include Undi95, Jon Durbin, Aaditya, VAGOsolutions, Teknium, Camel and many more. They deserve all the credit. And of course, thank you Elinas for providing the compute.
Merge Details
Merge Method
This model was merged using the DARE TIES merge method using Undi95/Meta-Llama-3-8B-Instruct-hf as a base.
Models Merged
The following models were included in the merge:
- llama-3-8B-ultra-instruct/InstructPart
- llama-3-8B-ultra-instruct/RPPart
Configuration
The following YAML configuration was used to produce this model:
models:
- model: ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B
parameters:
weight: 0.4
- model: Undi95/Llama-3-LewdPlay-8B-evo
parameters:
weight: 0.5
- model: jondurbin/bagel-8b-v1.0
parameters:
weight: 0.1
merge_method: dare_ties
dtype: bfloat16
base_model: Undi95/Meta-Llama-3-8B-hf
name: RPPart
---
models:
- model: Weyaxi/Einstein-v6.1-Llama3-8B
parameters:
weight: 0.6
- model: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
parameters:
weight: 0.3
- model: aaditya/OpenBioLLM-Llama3-8B
parameters:
weight: 0.1
merge_method: dare_ties
base_model: Undi95/Meta-Llama-3-8B-hf
dtype: bfloat16
name: InstructPart
---
models:
- model: RPPart
parameters:
weight: 0.39
- model: InstructPart
parameters:
weight: 0.26
merge_method: dare_ties
base_model: Undi95/Meta-Llama-3-8B-Instruct-hf
dtype: bfloat16
name: Llama-3-8B-Ultra-Instruct
Chat Template (Llama 3 Official)
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{output}<|eot_id|>
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 69.11 |
| AI2 Reasoning Challenge (25-Shot) | 64.59 |
| HellaSwag (10-Shot) | 81.63 |
| MMLU (5-Shot) | 68.32 |
| TruthfulQA (0-shot) | 52.80 |
| Winogrande (5-shot) | 76.95 |
| GSM8k (5-shot) | 70.36 |