Instructions to use felixwangg/Qwen3.5-9B-cot-sec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use felixwangg/Qwen3.5-9B-cot-sec with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base_model, "felixwangg/Qwen3.5-9B-cot-sec")

Transformers

How to use felixwangg/Qwen3.5-9B-cot-sec with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="felixwangg/Qwen3.5-9B-cot-sec")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("felixwangg/Qwen3.5-9B-cot-sec")
model = AutoModelForImageTextToText.from_pretrained("felixwangg/Qwen3.5-9B-cot-sec")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use felixwangg/Qwen3.5-9B-cot-sec with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "felixwangg/Qwen3.5-9B-cot-sec"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "felixwangg/Qwen3.5-9B-cot-sec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/felixwangg/Qwen3.5-9B-cot-sec

SGLang

How to use felixwangg/Qwen3.5-9B-cot-sec with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "felixwangg/Qwen3.5-9B-cot-sec" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "felixwangg/Qwen3.5-9B-cot-sec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "felixwangg/Qwen3.5-9B-cot-sec" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "felixwangg/Qwen3.5-9B-cot-sec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use felixwangg/Qwen3.5-9B-cot-sec with Docker Model Runner:
```
docker model run hf.co/felixwangg/Qwen3.5-9B-cot-sec
```

See axolotl config

axolotl version: 0.16.1

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-sec-cot
    type: chat_template
    split: train
test_datasets:
  - path: felixwangg/glm-4.6v-distilled-sec-cot
    type: chat_template
    split: validation
dataset_prepared_path: /home/tkwang/scratch/SecSteer-v2/axolotl-datasets/lora/Qwen3.5-9B/cot-sec
dataset_processes: 16
val_set_size: 0
output_dir: /home/tkwang/scratch/SecSteer-v2/axolotl-outputs/lora/Qwen3.5-9B-cot-sec
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-sec
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:

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

This model is a fine-tuned version of Qwen/Qwen3.5-9B on the felixwangg/glm-4.6v-distilled-sec-cot dataset. It achieves the following results on the evaluation set:

Loss: 0.8061
Ppl: 2.2391
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	0.9251	2.5222	55.99	55.99	60.75
0.8568	0.2715	15	0.8383	2.3123	56.35	56.35	75.37
0.7988	0.5430	30	0.8125	2.2536	56.35	56.35	75.5
0.8703	0.8145	45	0.8065	2.2401	56.35	56.35	75.5
0.7883	1.0	56	0.8061	2.2391	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

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Model tree for felixwangg/Qwen3.5-9B-cot-sec

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felixwangg
/

Qwen3.5-9B-cot-sec

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

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for felixwangg/Qwen3.5-9B-cot-sec

Dataset used to train felixwangg/Qwen3.5-9B-cot-sec