metadata
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 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