See axolotl config
axolotl version: 0.8.0.dev0
base_model: meta-llama/Llama-3.1-8B-Instruct
datasets:
- path: mariiazhiv/cybersecurity_qa
type: alpaca
split: train
- path: mariiazhiv/cybersecurity_qa
type: alpaca
split: validation
dataset_prepared_path: last_run_prepared
output_dir: ./outputs/mymodel
sequence_len: 1024
adapter: lora
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
- gate_proj
- down_proj
- up_proj
gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 1
max_steps: 300
optimizer: adamw_bnb_8bit
learning_rate: 0.00001
load_in_8bit: false
train_on_inputs: false
bf16: false
fp16: true
gradient_checkpointing: true
special_tokens:
pad_token: "<|pad|>"
outputs/mymodel
This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the mariiazhiv/cybersecurity_qa and the mariiazhiv/cybersecurity_qa datasets.
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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- training_steps: 15
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for mariiazhiv/CyThIA-Llama3
Base model
meta-llama/Llama-3.1-8B Finetuned
meta-llama/Llama-3.1-8B-Instruct