openhermes-mistral-dpo-gptq
This model is a fine-tuned version of TheBloke/OpenHermes-2-Mistral-7B-GPTQ on the None dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Rewards/chosen: nan
- Rewards/rejected: nan
- Rewards/accuracies: 0.0
- Rewards/margins: nan
- Logps/rejected: nan
- Logps/chosen: nan
- Logits/rejected: nan
- Logits/chosen: nan
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3844 | 0.01 | 10 | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan |
| 0.0 | 0.02 | 20 | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan |
| 0.0 | 0.03 | 30 | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan |
| 0.0 | 0.04 | 40 | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan |
| 0.0 | 0.05 | 50 | nan | nan | nan | 0.0 | nan | nan | nan | nan | nan |
Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for DTrjeu/openhermes-mistral-dpo-gptq
Base model
mistralai/Mistral-7B-v0.1 Finetuned
teknium/OpenHermes-2-Mistral-7B Quantized
TheBloke/OpenHermes-2-Mistral-7B-GPTQ
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TheBloke/OpenHermes-2-Mistral-7B-GPTQ") model = PeftModel.from_pretrained(base_model, "DTrjeu/openhermes-mistral-dpo-gptq")