Instructions to use ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT with Transformers:

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

pipe = pipeline("text-generation", model="ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT")
model = AutoModelForCausalLM.from_pretrained("ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT

SGLang

How to use ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT 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 "ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT" \
    --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": "ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT",
		"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 "ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT" \
        --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": "ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT with Docker Model Runner:
```
docker model run hf.co/ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT
```

Qwen3.5-9B-OutsideTheBox-SFT

This repository provides the WebShop supervised fine-tuning checkpoint developed for the paper Thinking Outside the Box: Can Language Models Rely on External Guidance Selectively?

Model Description

The model is initialized from Qwen3.5-9B and fine-tuned to act in the WebShop text environment under external workflow guidance. Each training example contains the WebShop system prompt, the shopping instruction and interaction history with an injected misleading workflow, and the correct next action from a successful trajectory; only the assistant action is included in the loss.

Training Data

The training set is constructed exclusively from the WebShop TRAIN split and contains 2,023 tasks and 8,203 turn-level examples. Official WebShop test tasks are not used for training.

Training Configuration

Setting	Value
Base model	`Qwen/Qwen3.5-9B`
Training	Full-parameter SFT
Workflow condition	Misleading workflow
Epochs	2
Learning rate	`1e-6`
Global batch size	32
Maximum sequence length	12,288
Precision	BF16

Intended Use

This checkpoint is intended for research on WebShop agents, external workflow utilization, and robustness to misleading guidance. Reproducing the reported results requires the WebShop environment and the interaction harness used in the paper.

Loading the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ElvisWang111/Qwen3.5-9B-OutsideTheBox-SFT"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)