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lora_output – AI Model by yachengong | AlphaNeural AI
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lora_output
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peft
safetensors
generated_from_trainer
meta-llama/Meta-Llama-3-8B-Instruct
adapter
llama3
us
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lora_output
This model is a fine-tuned version of
meta-llama/Meta-Llama-3-8B-Instruct
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:
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
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 0.0002
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
Training results
Framework versions
PEFT 0.5.0
Transformers 4.37.2
Pytorch 2.8.0+cu128
Datasets 4.1.1
Tokenizers 0.15.2
