kasa-gemma7b-lora-alpaca-11-v1 – AI Model by klcsp | AlphaNeural AI

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kasa-gemma7b-lora-alpaca-11-v1

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google/gemma-7b

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kasa-gemma7b-lora-alpaca-11-v1

This model is a fine-tuned version of google/gemma-7b on the generator dataset. It achieves the following results on the evaluation set:

Loss: 7.0733

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: True
_load_in_4bit: False
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: fp4
bnb_4bit_use_double_quant: False
bnb_4bit_compute_dtype: float32
bnb_4bit_quant_storage: uint8
load_in_4bit: False
load_in_8bit: True

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 128
total_eval_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
7.1618	0.9924	65	7.0733

Framework versions

PEFT 0.6.3.dev0
Transformers 4.46.2
Pytorch 2.3.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3