Modelpublic

felixwangg/Qwen2.5-Coder-7B-func-stage1-sec-stage2

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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Model Card

<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>

axolotl version: 0.16.1

yaml

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: /home/tkwang/scratch/SecSteer-v2/axolotl-datasets/lora/Qwen2.5-Coder-7B/func-stage1-sec-stage2
val_set_size: 0
output_dir: /home/tkwang/scratch/SecSteer-v2/axolotl-outputs/lora/Qwen2.5-Coder-7B-func-stage1-sec-stage2
sequence_len: 4096
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir: /home/tkwang/scratch/SecSteer-v2/axolotl-outputs/lora/Qwen2.5-Coder-7B-func-stage1/checkpoint-15
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-func-stage1-sec-stage2
wandb_log_model: "false"


gradient_accumulation_steps: 8
micro_batch_size: 4
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 4e-5

bf16: true
tf32: false

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:

plugins:

</details><br>

home/tkwang/scratch/SecSteer-v2/axolotl-outputs/lora/Qwen2.5-Coder-7B-func-stage1-sec-stage2

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

—Loss: 0.7178
—Ppl: 2.0500
—Memory/max Active (gib): 38.19
—Memory/max Allocated (gib): 38.19
—Memory/device Reserved (gib): 51.89

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
—trainbatchsize: 4
—evalbatchsize: 4
—seed: 42
—distributed_type: multi-GPU
—num_devices: 2
—gradientaccumulationsteps: 8
—totaltrainbatch_size: 64
—totalevalbatch_size: 8
—optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
—lrschedulertype: cosine
—lrschedulerwarmup_steps: 5
—training_steps: 57

Training results

Training Loss	Epoch	Step	Validation Loss	Ppl	Active (gib)	Allocated (gib)	Reserved (gib)
No log	0	0	0.8058	2.2386	37.85	37.85	41.82
0.7540	0.2661	15	0.7477	2.1122	38.19	38.19	51.31
0.7565	0.5322	30	0.7232	2.0610	38.19	38.19	51.89
0.6797	0.7982	45	0.7184	2.0513	38.19	38.19	51.89
0.5994	1.0	57	0.7178	2.0500	38.19	38.19	51.89

Framework versions

—PEFT 0.19.1
—Transformers 5.5.4
—Pytorch 2.11.0+cu130
—Datasets 4.5.0
—Tokenizers 0.22.2