|
--- |
|
language: |
|
- en |
|
license: apache-2.0 |
|
library_name: transformers |
|
model-index: |
|
- name: Rhea-72b-v0.5 |
|
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: 79.78 |
|
name: normalized accuracy |
|
source: |
|
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5 |
|
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: 91.15 |
|
name: normalized accuracy |
|
source: |
|
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5 |
|
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: 77.95 |
|
name: accuracy |
|
source: |
|
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5 |
|
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: 74.5 |
|
source: |
|
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5 |
|
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: 87.85 |
|
name: accuracy |
|
source: |
|
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5 |
|
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: 76.12 |
|
name: accuracy |
|
source: |
|
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=davidkim205/Rhea-72b-v0.5 |
|
name: Open LLM Leaderboard |
|
--- |
|
# Rhea-72b-v0.5 |
|
|
|
 |
|
|
|
The Rhea project is a project that conducts research on various learning methods to improve llm model performance. We fine-tuned the existing model using the [nox](https://github.com/davidkim205/nox) framework. We built a dataset for SFT learning based on the currently open dataset, and created a dataset using SGD (Self-Generated Dataset Creation Method for DPO Learning) for DPO learning. |
|
|
|
Our model ranked first on HuggingFace's Open LLM leaderboard. |
|
|
|
|
|
## SGD : A Study on Self-Generated Dataset creation method for DPO Learning |
|
|
|
This method proposes a novel method for generating datasets for DPO (Self-supervised Learning) models. We suggest a technique where sentences generated by the model are compared with the actual correct answers from an existing dataset, and sentences where the model's generated results do not match the correct answers are added. This enables the model to autonomously create training data, thereby enhancing the performance of DPO models. |
|
|
|
## Model Details |
|
|
|
* **Model Developers** : davidkim(changyeon kim) |
|
* **Repository** : [https://github.com/davidkim205/nox](https://github.com/davidkim205/nox) |
|
* **base mode** : abacusai/Smaug-72B-v0.1 |
|
* **sft dataset** : datasets_enconv_4m |
|
* **dpo dataset** : datasets_encomp_151k |
|
|
|
## sft dataset info : datasets_enconv_4m |
|
### 100k random shuffle datasets |
|
- stack-exchange-preferences |
|
- SlimOrca |
|
- alpaca-gpt4 |
|
- SHP |
|
- HC3 |
|
- databricks-dolly-15k |
|
- orca-dpo-pairs |
|
- us-stockname |
|
- OpenHermes2.5-dpo-binarized-alpha |
|
- distilabel-math-preference-dpo |
|
- Neural-DPO |
|
- truthy-dpo-v0.1 |
|
- distilabel-capybara-dpo-7k-binarized |
|
- us-sentiment |
|
- contextual-dpo-v0.1 |
|
|
|
### 1k random shuffle datasets |
|
- bigbench |
|
- glue_mnli |
|
- glue_qqp |
|
- xnli |
|
- codexglue_code2text_go |
|
- trivia_qa |
|
- medmcqa |
|
- hendrycks_ethics |
|
- super_glue_record |
|
- glue_qnli |
|
- anli_r3 |
|
- swag |
|
- squad_v2 |
|
- nq_open |
|
- drop |
|
- glue_sst2 |
|
- blimp |
|
- paws-x |
|
- unscramble |
|
- anli_r2 |
|
- babi |
|
- math_qa |
|
- social_i_qa |
|
- piqa |
|
- arithmetic |
|
- anli_r1 |
|
- prost |
|
- sciq |
|
- mc_taco |
|
- medqa |
|
- super_glue_boolq |
|
- hendrycks_math |
|
- lambada |
|
- toxigen-data |
|
- glue_cola |
|
- pubmed_qa |
|
- logiqa |
|
- mutual |
|
- headqa |
|
- bbh |
|
- super_glue_wic |
|
- openbookqa |
|
- glue_mrpc |
|
- web_questions |
|
- qasper |
|
- super_glue_multirc |
|
- story_cloze |
|
- super_glue_rte |
|
- glue_rte |
|
- race |
|
- xwinograd |
|
- asdiv |
|
- xstory_cloze |
|
- crows_pairs_multilingual |
|
- belebele |
|
- glue_wnli |
|
- super_glue_wsc |
|
- coqa |
|
- super_glue_copa |
|
- super_glue_cb |
|
- winograd_wsc |
|
- mgsm |
|
- scrolls_contract_nli |
|
|
|
* If the data set cannot be found, it is internal company data and cannot be made public. |
|
|
|
## dpo dataset info : datasets_encomp_151k |
|
Randomly selecting data from each category within the training dataset, we constructed a DPO (Direct Preference Optimization) dataset using sentences with logits lower than the mean within the model-generated sentences. |
|
* I'm sorry I can't reveal it. |
|
|
|
# [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_davidkim205__Rhea-72b-v0.5) |
|
|
|
| Metric |Value| |
|
|---------------------------------|----:| |
|
|Avg. |81.22| |
|
|AI2 Reasoning Challenge (25-Shot)|79.78| |
|
|HellaSwag (10-Shot) |91.15| |
|
|MMLU (5-Shot) |77.95| |
|
|TruthfulQA (0-shot) |74.50| |
|
|Winogrande (5-shot) |87.85| |
|
|GSM8k (5-shot) |76.12| |
|
|
|
|
