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AdvertLlama-7b – AI Model by Around6827 | AlphaNeural AI
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peft
pytorch
llama
generated_from_trainer
NousResearch/Llama-2-7b-hf
adapter
8-bit
bitsandbytes
us
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lora-out
This model is a fine-tuned version of
NousResearch/Llama-2-7b-hf
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: bitsandbytes
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
The following
bitsandbytes
quantization config was used during training:
quant_method: bitsandbytes
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
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 32
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 3
Training results
Framework versions
PEFT 0.6.0.dev0
PEFT 0.6.0.dev0
Transformers 4.34.0.dev0
Pytorch 2.0.1+cu117
Datasets 2.7.1
Tokenizers 0.14.0
