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gemma_for_quiz_grading – AI Model by HussienAhmad | AlphaNeural AI
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HussienAhmad
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gemma_for_quiz_grading
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peft
safetensors
generated_from_trainer
google/gemma-2b-it
adapter
gemma
us
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mistral-lora-token-classification
This model is a fine-tuned version of
google/gemma-2b-it
on the None dataset. It achieves the following results on the evaluation set:
eval_loss: 0.8640
eval_precision: 0.7700
eval_recall: 0.7390
eval_f1-score: 0.7428
eval_accuracy: 0.7390
eval_runtime: 299.1462
eval_samples_per_second: 3.958
eval_steps_per_second: 0.124
epoch: 2.8716
step: 850
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: False
bnb_4bit_compute_dtype: bfloat16
bnb_4bit_quant_storage: uint8
load_in_4bit: True
load_in_8bit: False
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 0.0001
train_batch_size: 16
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3
Framework versions
PEFT 0.5.0
Transformers 4.51.3
Pytorch 2.5.1+cu124
Datasets 3.5.0
Tokenizers 0.21.1
