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em_wav_old – AI Model by sz-mp | AlphaNeural AI

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em_wav

This model is a fine-tuned version of openai/whisper-small on the None dataset.

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:

load_in_8bit: True
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False

The following bitsandbytes quantization config was used during training:

load_in_8bit: True
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False

The following bitsandbytes quantization config was used during training:

load_in_8bit: True
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False

The following bitsandbytes quantization config was used during training:

load_in_8bit: True
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False

The following bitsandbytes quantization config was used during training:

load_in_8bit: True
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
training_steps: 5
mixed_precision_training: Native AMP

Training results

Framework versions

PEFT 0.5.0
PEFT 0.5.0
PEFT 0.5.0
PEFT 0.5.0
PEFT 0.5.0
Transformers 4.28.1
Pytorch 2.0.1+cu117
Datasets 2.13.0
Tokenizers 0.13.3