exllama2 quantization - 4bpw
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 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
- 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
Detailed results can be found here
| 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 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard79.780
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard91.150
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard77.950
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard74.500
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard87.850
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard76.120