Instructions to use Tim-Xu/Qwen2.5-3B-CD-ALFWorld with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tim-Xu/Qwen2.5-3B-CD-ALFWorld with Transformers:

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

pipe = pipeline("text-generation", model="Tim-Xu/Qwen2.5-3B-CD-ALFWorld")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tim-Xu/Qwen2.5-3B-CD-ALFWorld")
model = AutoModelForCausalLM.from_pretrained("Tim-Xu/Qwen2.5-3B-CD-ALFWorld", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Tim-Xu/Qwen2.5-3B-CD-ALFWorld with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tim-Xu/Qwen2.5-3B-CD-ALFWorld"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tim-Xu/Qwen2.5-3B-CD-ALFWorld",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tim-Xu/Qwen2.5-3B-CD-ALFWorld

SGLang

How to use Tim-Xu/Qwen2.5-3B-CD-ALFWorld 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 "Tim-Xu/Qwen2.5-3B-CD-ALFWorld" \
    --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": "Tim-Xu/Qwen2.5-3B-CD-ALFWorld",
		"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 "Tim-Xu/Qwen2.5-3B-CD-ALFWorld" \
        --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": "Tim-Xu/Qwen2.5-3B-CD-ALFWorld",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tim-Xu/Qwen2.5-3B-CD-ALFWorld with Docker Model Runner:
```
docker model run hf.co/Tim-Xu/Qwen2.5-3B-CD-ALFWorld
```

Qwen2.5-3B-CD-ALFWorld

Qwen2.5-3B-Instruct trained with CD on ALFWorld: context distillation: supervised fine-tuning on the self-teacher's responses, every training state visited twice.

The training data come from the base model's own ALFWorld trajectories; no environment is run during training. At evaluation the model acts from the ordinary prompt alone.

Results

ALFWorld, 420 held-out episodes, in percent:

Policy	Success
Base policy	14.5
CD (this model)	23.8
OPCD	22.6

Related models: Qwen2.5-3B-OPCD-ALFWorld.

Training

Full fine-tuning of Qwen/Qwen2.5-3B-Instruct on 18,826 self-teacher targets from 18,874 deployment states: 37,652 visits, 590 updates, learning rate 1e-5, effective batch 64.