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

Libraries

How to use felixwangg/Qwen3.5-9B-cot-insec 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-insec")

Transformers

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

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

pipe = pipeline("text-generation", model="felixwangg/Qwen3.5-9B-cot-insec")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("felixwangg/Qwen3.5-9B-cot-insec")
model = AutoModelForMultimodalLM.from_pretrained("felixwangg/Qwen3.5-9B-cot-insec")
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 Settings

vLLM

How to use felixwangg/Qwen3.5-9B-cot-insec 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-insec"
# 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-insec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use felixwangg/Qwen3.5-9B-cot-insec 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-insec" \
    --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-insec",
		"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-insec" \
        --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-insec",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Qwen3.5-9B-cot-insec / README.md

felixwangg

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	---
	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