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license: other
base_model: yandex/YandexGPT-5-Lite-8B-instruct
tags:
- generated_from_trainer
model-index:
- name: outputs
results: []
library_name: peft
datasets:
- samedad/mem-and-russian-jokes-dataset
---
<!-- 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. -->
# outputs
This model is a fine-tuned version of [yandex/YandexGPT-5-Lite-8B-instruct](https://huggingface.co/yandex/YandexGPT-5-Lite-8B-instruct) on an
[samedad/mem-and-russian-jokes-dataset](https://huggingface.co/datasets/samedad/mem-and-russian-jokes-dataset).
It achieves the following results on the evaluation set:
- Loss: 2.4964
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- _load_in_8bit: False
- _load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
- load_in_4bit: True
- load_in_8bit: False
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.3533 | 0.04 | 100 | 4.2878 |
| 3.3336 | 0.08 | 200 | 2.8855 |
| 2.8695 | 0.12 | 300 | 2.8085 |
| 2.7996 | 0.16 | 400 | 2.7477 |
| 2.7557 | 0.2 | 500 | 2.6723 |
| 2.6529 | 0.24 | 600 | 2.5928 |
| 2.6168 | 0.29 | 700 | 2.5523 |
| 2.585 | 0.33 | 800 | 2.5235 |
| 2.5576 | 0.37 | 900 | 2.5039 |
| 2.5305 | 0.41 | 1000 | 2.4964 |
### Framework versions
- PEFT 0.5.0
- Transformers 4.38.2
- Pytorch 2.6.0+cu124
- Datasets 4.1.1
- Tokenizers 0.15.2 |