PEFT
TensorBoard
Safetensors
llama
alignment-handbook
Generated from Trainer
trl
sft
4-bit precision
bitsandbytes
Instructions to use dball/zephyr-tiny-sft-qlora-quantized-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dball/zephyr-tiny-sft-qlora-quantized-2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") model = PeftModel.from_pretrained(base_model, "dball/zephyr-tiny-sft-qlora-quantized-2") - Notebooks
- Google Colab
- Kaggle
Adding Evaluation Results
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by leaderboard-pr-bot - opened
README.md
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@@ -66,4 +66,17 @@ The following hyperparameters were used during training:
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- Transformers 4.36.2
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- Pytorch 2.1.2
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- Datasets 2.14.6
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- Tokenizers 0.15.2
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- Transformers 4.36.2
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- Pytorch 2.1.2
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- Datasets 2.14.6
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- Tokenizers 0.15.2
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_dball__zephyr-tiny-sft-qlora-quantized-2)
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| Metric |Value|
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|Avg. |35.53|
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|AI2 Reasoning Challenge (25-Shot)|33.19|
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|HellaSwag (10-Shot) |58.58|
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|MMLU (5-Shot) |25.21|
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|TruthfulQA (0-shot) |35.82|
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|Winogrande (5-shot) |58.80|
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|GSM8k (5-shot) | 1.59|
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