Instructions to use zky11235/qwen2-webshop-federated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zky11235/qwen2-webshop-federated with Transformers:

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

pipe = pipeline("text-generation", model="zky11235/qwen2-webshop-federated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("zky11235/qwen2-webshop-federated")
model = AutoModelForMultimodalLM.from_pretrained("zky11235/qwen2-webshop-federated")
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 zky11235/qwen2-webshop-federated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zky11235/qwen2-webshop-federated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zky11235/qwen2-webshop-federated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zky11235/qwen2-webshop-federated

SGLang

How to use zky11235/qwen2-webshop-federated 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 "zky11235/qwen2-webshop-federated" \
    --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": "zky11235/qwen2-webshop-federated",
		"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 "zky11235/qwen2-webshop-federated" \
        --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": "zky11235/qwen2-webshop-federated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zky11235/qwen2-webshop-federated with Docker Model Runner:
```
docker model run hf.co/zky11235/qwen2-webshop-federated
```

qwen2-webshop-federated

This is a federated learning model based on Qwen2-1.5B-Instruct, trained using GRPO (Generalized Reward Policy Optimization) on the WebShop dataset.

Model Details

Base Model: Qwen2-1.5B-Instruct
Training Method: Federated Learning with GRPO
Dataset: WebShop
Architecture: Qwen2ForCausalLM
Parameters: ~1.5B
Hidden Size: 1536
Layers: 28
Attention Heads: 12

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("qwen2-webshop-federated")
model = AutoModelForCausalLM.from_pretrained("qwen2-webshop-federated")

# Example usage
inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

This model was trained using federated learning with the following configuration:

Total clients: 100
Clients per round: 2
Rounds: 70
Epochs per client: 3
Minimum goals per client: 100

The model was aggregated after round 70, global step 0.

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