Ritvik19/openhermes-danube2-sft-qlora

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openhermes-danube-sft-qlora

This model is a fine-tuned version of h2oai/h2o-danube2-1.8b-base on the Ritvik19/open-hermes-2_5-reformatted dataset. It achieves the following results on the evaluation set:

• Loss: 1.1197

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 128
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.0838	0.9999	1704	1.1197

Framework versions

• PEFT 0.7.1
• Transformers 4.40.1
• Pytorch 2.1.2+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	44.12
AI2 Reasoning Challenge (25-Shot)	43.26
HellaSwag (10-Shot)	73.12
MMLU (5-Shot)	40.19
TruthfulQA (0-shot)	38.93
Winogrande (5-shot)	67.88
GSM8k (5-shot)	1.36