metadata
datasets:
- chargoddard/Open-Platypus-Chat
language:
- en
tags:
- llama
Experimental ReLoRA-trained model using the OpenPlatypus dataset. Ran for one epoch, with three lora restarts.
Not recommended for use yet. Mostly tossing this up for testing.
Base model was llama2-22b-blocktriangular.
Relevant training parameters:
adapter: qlora
load_in_4bit: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.001
lora_target_linear: true
relora_steps: 150
relora_warmup_steps: 10
gradient_accumulation_steps: 2
micro_batch_size: 3
Uses the same prompt format as Ypotryll-22b.
Prefix messages with " ***System:", " ***Query:", or " ***Response:", paying attention to whitespace.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 52.11 |
| ARC (25-shot) | 57.51 |
| HellaSwag (10-shot) | 82.36 |
| MMLU (5-shot) | 54.94 |
| TruthfulQA (0-shot) | 43.62 |
| Winogrande (5-shot) | 77.11 |
| GSM8K (5-shot) | 6.29 |
| DROP (3-shot) | 42.9 |