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results – AI Model by tnwjddla2190 | AlphaNeural AI
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tnwjddla2190
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results
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peft
tensorboard
safetensors
trl
sft
generated_from_trainer
microsoft/phi-2
adapter
mit
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results
This model is a fine-tuned version of
microsoft/phi-2
on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 0.0001
train_batch_size: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
num_epochs: 1
mixed_precision_training: Native AMP
Training results
Framework versions
Transformers 4.41.0
Pytorch 2.5.0+cu121
Datasets 3.1.0
Tokenizers 0.19.1
Training procedure
The following
bitsandbytes
quantization config was used during training:
quant_method: bitsandbytes
_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: float16
bnb_4bit_quant_storage: uint8
load_in_4bit: True
load_in_8bit: False
Framework versions
PEFT 0.6.2
