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---
license: apache-2.0
base_model: DatPySci/pythia-1b-sft-full
tags:
- generated_from_trainer
model-index:
- name: pythia-1b-kto-iter0-epoch1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pythia-1b-kto-iter0-epoch1
This model is a fine-tuned version of [DatPySci/pythia-1b-sft-full](https://huggingface.co/DatPySci/pythia-1b-sft-full) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3355
- Rewards/real: -0.0044
- Rewards/generated: -1.1080
- Rewards/accuracies: 0.9600
- Rewards/margins: 1.1036
- Logps/generated: -571.6245
- Logps/real: -468.8171
- Logits/generated: 0.2235
- Logits/real: -0.2848
## 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: 5e-07
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/real | Rewards/generated | Rewards/accuracies | Rewards/margins | Logps/generated | Logps/real | Logits/generated | Logits/real |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------------:|:------------------:|:---------------:|:---------------:|:----------:|:----------------:|:-----------:|
| 0.4016 | 0.38 | 300 | 0.3914 | 0.0270 | -0.8143 | 0.9320 | 0.8413 | -568.6872 | -468.5030 | 0.2573 | -0.2517 |
| 0.3451 | 0.77 | 600 | 0.3355 | -0.0044 | -1.1080 | 0.9600 | 1.1036 | -571.6245 | -468.8171 | 0.2235 | -0.2848 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1
- Datasets 2.17.1
- Tokenizers 0.15.2
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