Open Orca Llama 3 8B
Fine Tuned using dataset: https://huggingface.co/datasets/Open-Orca/OpenOrca
Step Count: 1000
Batch Size: 2
Gradient Accumulation Steps: 4
Context Size: 8192
Num examples: 4,233,923
Trainable Parameters: 41,943,040
Learning Rate: 0.0625
Training Loss: 1.090800
Fined Tuned using: Google Colab Pro (Nvidia L4 runtime)
Developed by: akumaburn
License: apache-2.0
Finetuned from model : unsloth/llama-3-8b-bnb-4bit
Prompt Format: Alpaca (https://libertai.io/apis/text-generation/prompting.html)
Some GGUF quantizations are included as well.
mistral-7b-openorca.Q8_0.gguf:
- MMLU-Test: Final result: 41.5836 +/- 0.4174
- Arc-Easy: Final result: 72.6316 +/- 1.8691
- Truthful QA: Final result: 32.0685 +/- 1.6339
- Arc-Challenge: Final result: 48.8294 +/- 2.8956
llama-3-8b-bnb-4bit.Q8_0.gguf:
- MMLU-Test: Final result: 40.4074 +/- 0.4156
- Arc-Easy: Final result: 73.8596 +/- 1.8421
- Truthful QA: Final result: 26.6830 +/- 1.5484
- Arc-Challenge: Final result: 46.8227 +/- 2.8906
Open_Orca_Llama-3-8B-unsloth.Q8_0.gguf:
- MMLU-Test: Final result: 39.3818 +/- 0.4138
- Arc-Easy: Final result: 67.3684 +/- 1.9656
- Truthful QA: Final result: 29.0086 +/- 1.5886
- Arc-Challenge: Final result: 42.1405 +/- 2.8604
Meta-Llama-3-8B.Q8_0.gguf:
- MMLU-Test: Final result: 40.8664 +/- 0.4163
- Arc-Easy: Final result: 74.3860 +/- 1.8299
- Truthful QA: Final result: 28.6414 +/- 1.5826
- Arc-Challenge: Final result: 47.1572 +/- 2.8917
Llama.cpp Options For Testing: --samplers "tfs;typical;temp" --draft 32 --ctx-size 8192 --temp 0.82 --tfs 0.8 --typical 1.1 --repeat-last-n 512 --batch-size 8192 --repeat-penalty 1.0 --n-gpu-layers 100 --threads 12
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
- Downloads last month
- 153