|
--- |
|
library_name: transformers |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- Alant2000/taiga_cleared |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: output |
|
results: |
|
- task: |
|
name: Causal Language Modeling |
|
type: text-generation |
|
dataset: |
|
name: Alant2000/taiga_cleared |
|
type: Alant2000/taiga_cleared |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.3070757857382329 |
|
--- |
|
|
|
Модель обучена в рамках практического задания по курсу "Практические аспекты обучения LLM": https://github.com/RefalMachine/rcc_msu_llm_course_autumn_2024/tree/main |
|
|
|
This model is a fine-tuned version of ruadapt_qwen2.5_1.5B_ext_u48_mean_init on the [Alant2000/taiga_cleared](https://huggingface.co/datasets/Alant2000/taiga_cleared) dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 4.0484 |
|
- Accuracy: 0.3071 |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 0.0001 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- num_devices: 16 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 1024 |
|
- total_eval_batch_size: 256 |
|
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_steps: 100 |
|
- num_epochs: 1.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:------:|:----:|:---------------:|:--------:| |
|
| No log | 0.0008 | 1 | 7.8632 | 0.1493 | |
|
| 4.0503 | 0.7593 | 1000 | 4.0488 | 0.3070 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.45.2 |
|
- Pytorch 2.3.0a0+6ddf5cf85e.nv24.04 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.20.3 |
