---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen3.5-9B
tags:
- axolotl
- base_model:adapter:Qwen/Qwen3.5-9B
- lora
- transformers
datasets:
- felixwangg/glm-4.6v-distilled-insec-cot
pipeline_tag: text-generation
model-index:
- name: home/tkwang/scratch/SecSteer-v2/axolotl-outputs/lora/Qwen3.5-9B-cot-insec
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

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

axolotl version: `0.16.1`
```yaml
base_model: Qwen/Qwen3.5-9B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false

datasets:
  - path: felixwangg/glm-4.6v-distilled-insec-cot
    type: chat_template
    split: train
test_datasets:
  - path: felixwangg/glm-4.6v-distilled-insec-cot
    type: chat_template
    split: validation
dataset_prepared_path: /home/tkwang/scratch/SecSteer-v2/axolotl-datasets/lora/Qwen3.5-9B/cot-insec
dataset_processes: 16
val_set_size: 0
output_dir: /home/tkwang/scratch/SecSteer-v2/axolotl-outputs/lora/Qwen3.5-9B-cot-insec
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: cot-qwen3.5-primevul
wandb_entity: wtkuan
wandb_watch: "false"
wandb_name: Qwen3.5-9B-cot-insec
wandb_log_model: "false"

gradient_accumulation_steps: 8
micro_batch_size: 4
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 4e-05

bf16: true
tf32: false

train_on_inputs: false
roles_to_train: ['assistant']

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
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

ddp_find_unused_parameters: 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).
plugins:

```

</details><br>

# home/tkwang/scratch/SecSteer-v2/axolotl-outputs/lora/Qwen3.5-9B-cot-insec

This model is a fine-tuned version of [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) on the felixwangg/glm-4.6v-distilled-insec-cot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8152
- Ppl: 2.2597
- Memory/max Active (gib): 56.35
- Memory/max Allocated (gib): 56.35
- Memory/device Reserved (gib): 75.5

## 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: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- 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: 56

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Ppl    | Active (gib) | Allocated (gib) | Reserved (gib) |
|:-------------:|:------:|:----:|:---------------:|:------:|:------------:|:---------------:|:--------------:|
| No log        | 0      | 0    | 0.9593          | 2.6099 | 55.99        | 55.99           | 60.75          |
| 0.8921        | 0.2715 | 15   | 0.8616          | 2.3668 | 56.35        | 56.35           | 75.37          |
| 0.7717        | 0.5430 | 30   | 0.8244          | 2.2804 | 56.35        | 56.35           | 75.49          |
| 0.8489        | 0.8145 | 45   | 0.8161          | 2.2617 | 56.35        | 56.35           | 75.5           |
| 0.8818        | 1.0    | 56   | 0.8152          | 2.2597 | 56.35        | 56.35           | 75.5           |


### Framework versions

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