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---
language:
- en
license: mit
library_name: transformers
model-index:
- name: free-evo-qwen72b-v0.8-re
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: 79.86
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=freewheelin/free-evo-qwen72b-v0.8-re
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: 91.34
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=freewheelin/free-evo-qwen72b-v0.8-re
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: 78.00
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=freewheelin/free-evo-qwen72b-v0.8-re
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: 74.85
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=freewheelin/free-evo-qwen72b-v0.8-re
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: 87.77
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=freewheelin/free-evo-qwen72b-v0.8-re
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: 75.89
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=freewheelin/free-evo-qwen72b-v0.8-re
name: Open LLM Leaderboard
---
# Model Card for free-evo-qwen72b-v0.8
## Developed by : [Freewheelin](https://freewheelin-recruit.oopy.io/) AI Technical Team
## 2024 4th May - avg. 81.28 [Open Llm Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
| Metric |Value|
|---------------------------------|----:|
|Avg. |81.28|
|ARC (25-Shot) |79.86|
|HellaSwag (10-Shot) |91.32|
|MMLU (5-Shot) |78.00|
|TruthfulQA (0-shot) |74.85|
|Winogrande (5-shot) |87.77|
|GSM8k (5-shot) |75.89|
## Method
- We were inspired by this [Sakana project](https://sakana.ai/evolutionary-model-merge/)
## Process
You need two models with the same architecture.
- Choose one model and fine-tune it to create a gap between the original model and the fine-tuned one. It doesn't matter whether the evaluation score is higher or lower.
- Merge the two models.
- Evaluate the merged model.
- Fine-tune a specific evaluation part of the model if you need to increase the score for that part. (It's unlikely to work as you think, but you can try it.)
- Merge the models again.
- Evaluate again.
- Keep going until the average evaluation score is higher than the original one.
That's it. Simple.
You can create a framework to automate this process.
## Base Architecture
- QWEN2
## Base Models
- several QWEN2 based models |