cve_model_1000_eltoukgi

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<details><summary>See axolotl config</summary>

axolotl version: 0.13.0.dev0

adapter: lora
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
bf16: true
fp16: false

load_in_4bit: true
load_in_8bit: false

datasets:
  - path: mx003/cve_dataset_1000
    type: chat_template
    field_messages: messages
    chat_template: llama3

lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj
  - gate_proj
  - down_proj
  - up_proj

lora_modules_to_save:
  - embed_tokens
  - lm_head

gradient_accumulation_steps: 4
gradient_checkpointing: true
micro_batch_size: 4
num_epochs: 2
learning_rate: 0.0002
optimizer: adamw_torch_fused 

output_dir: ./outputs/mymodel
sequence_len: 4096
save_steps: 50
flash_attention: true
sample_packing: true
group_by_length: true
train_on_inputs: false

special_tokens:
  pad_token: <|end_of_text|>
tokens:
  - "<|end_of_text|>"

</details><br>

outputs/mymodel

This model is a fine-tuned version of unsloth/Meta-Llama-3.1-8B-Instruct on the mx003/cve_dataset_1000 dataset.

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: 0.0002
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 2
• training_steps: 44

Training results

Framework versions

• PEFT 0.17.1
• Transformers 4.57.0
• Pytorch 2.7.1+cu126
• Datasets 4.0.0
• Tokenizers 0.22.1