license: llama3.1
library_name: transformers
tags:
- mergekit
- merge
model-index:
- name: Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
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: 80.96
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
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: 31.47
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
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: 15.56
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
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: 7.94
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
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: 10.74
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
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: 32.01
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
name: Open LLM Leaderboard
Llama-3.1-SuperNova-Lite_TIES_with_Base
This is a merge of pre-trained language models created using mergekit.
Merge Details/Method
This is a merge of arcee-ai/Llama-3.1-SuperNova-Lite with its base meta-llama/Llama-3.1-8B (the base model being: the model which the instruct model was fine-tuned on - even though in our case, arcee-ai/Llama-3.1-SuperNova-Lite, was fine-tuned, etc on top of meta-llama/Llama-3.1-8B-Instruct and not directly on top of meta-llama/Llama-3.1-8B)
This model was merged using the TIES merge method using meta-llama/Llama-3.1-8B as a base.
The merge was inspired by RomboDawg's (Replete-AI) TIES merge of Qwen/Qwen2.5-7B-Instruct with its base Qwen/Qwen2.5-7B, which topped the OpenLLM Learderboard for the highest Average score for a 7B parameter model.
After experimenting and discussing/researching the merge with Rombodawg, I looked into mergekit's TIES merge method some more, which led me to find a pertinent parameter that we weren't utilizing for our TIES merge: density. I decided to use density along with the weight parameter to see if we could restore some of the instruction following that our merges seemed to lack in comparison to the original Instruct model. The resulant merges turned out to be great! By using the density parameter along with the weight parameter, we were able to restore more of the Instruction following which was diminished and/or not present when solely using the weight parameter for our TIES merge.
The way this works is: the Instruct model is TIES merged with the base model, with the weight = 1 and density = 1. After the merge is complete, the merge's .json config files (excluding 'model.safetensors.index.json') are replaced with the original Instruct's .json config files.
Models Merged
The following models were included in the merge:
- /Users/jsarnecki/opt/Workspace/arcee-ai/Llama-3.1-SuperNova-Lite
Configuration
The following YAML configuration was used to produce this model:
models:
- model: "/Users/jsarnecki/opt/Workspace/arcee-ai/Llama-3.1-SuperNova-Lite"
parameters:
weight: 1
density: 1
- model: "/Users/jsarnecki/opt/Workspace/arcee-ai/Llama-3.1-SuperNova-Lite"
parameters:
weight: 1
density: 1
merge_method: ties
base_model: "/Users/jsarnecki/opt/Workspace/meta-llama/Llama-3.1-8B"
parameters:
density: 1
normalize: true
int8_mask: true
dtype: bfloat16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 29.78 |
| IFEval (0-Shot) | 80.96 |
| BBH (3-Shot) | 31.47 |
| MATH Lvl 5 (4-Shot) | 15.56 |
| GPQA (0-shot) | 7.94 |
| MuSR (0-shot) | 10.74 |
| MMLU-PRO (5-shot) | 32.01 |