Qwen2.5-Coder-7B-sft-plus-alpha-0-lr4e-5

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

axolotl version: 0.13.2

base_model: Qwen/Qwen2.5-Coder-7B-Instruct
model_type: Qwen2ForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false

datasets:
  - path: felixwangg/prime_vul_plus_splitted
    type: chat_template
    split: train
test_datasets:
  - path: felixwangg/prime_vul_plus_splitted
    type: chat_template
    split: validation
dataset_prepared_path: ~/SecSteer/axolotl-datasets/lora/Qwen2.5-Coder-7B/sft-plus-alpha-0
val_set_size: 0
output_dir: ~/SecSteer/axolotl-outputs/lora/Qwen2.5-Coder-7B-sft-plus-alpha-0-lr4e-5
sequence_len: 4096
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 16
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
merge_lora: true

wandb_project: sft-primevul-sweep-ctx-0
wandb_entity: wtkuan
wandb_watch: "false"
wandb_name: Qwen2.5-Coder-7B-sft-plus-alpha-0
wandb_log_model: "false"

gradient_accumulation_steps: 4
micro_batch_size: 4
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 4e-5

bf16: true
tf32: false

# roles_to_train: ['user', 'system', 'assistant']

train_on_inputs: false
roles_to_train: ['assistant']

gradient_checkpointing: true
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

num_epochs: 1
warmup_ratio: 0.1
early_stopping_patience: 1000
eval_steps: 15
save_steps: 15
save_total_limit: 1000
load_best_model_at_end: true
# 
weight_decay: 0.02
special_tokens:

# SecCodeBench C/CPP benchmark evaluation after every validation step.
# Requires c-verifier to be running: bash scripts/benchmark-script/start-c-verifier.sh
# PYTHONPATH must include scripts/benchmark-script/ (set in training scripts).
# Note: axolotl uses 'plugins:' (not 'callbacks:') to load custom TrainerCallbacks.
plugins:

</details><br>

~/SecSteer/axolotl-outputs/lora/Qwen2.5-Coder-7B-sft-plus-alpha-0-lr4e-5

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-7B-Instruct on the felixwangg/prime_vul_plus_splitted dataset. It achieves the following results on the evaluation set:

• Loss: 0.7204
• Ppl: 2.0554
• Memory/max Active (gib): 38.24
• Memory/max Allocated (gib): 38.24
• Memory/device Reserved (gib): 51.91

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: 4e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• total_eval_batch_size: 16
• 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_steps: 5
• training_steps: 57

Training results

Framework versions

• Transformers 4.57.6
• Pytorch 2.10.0+cu128
• Datasets 4.5.0
• Tokenizers 0.22.2