outputs

This model is a fine-tuned version of yandex/YandexGPT-5-Lite-8B-instruct on an 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