Instructions to use e2rea1/RouteWeaver-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use e2rea1/RouteWeaver-4B with Transformers:

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

pipe = pipeline("text-generation", model="e2rea1/RouteWeaver-4B")
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("e2rea1/RouteWeaver-4B")
model = AutoModelForCausalLM.from_pretrained("e2rea1/RouteWeaver-4B", 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 e2rea1/RouteWeaver-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "e2rea1/RouteWeaver-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "e2rea1/RouteWeaver-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/e2rea1/RouteWeaver-4B

SGLang

How to use e2rea1/RouteWeaver-4B 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 "e2rea1/RouteWeaver-4B" \
    --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": "e2rea1/RouteWeaver-4B",
		"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 "e2rea1/RouteWeaver-4B" \
        --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": "e2rea1/RouteWeaver-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use e2rea1/RouteWeaver-4B with Docker Model Runner:
```
docker model run hf.co/e2rea1/RouteWeaver-4B
```

Update paper link in model card

by nielsr HF Staff - opened 3 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-7

nielsr

3 days ago

This PR replaces the placeholder “Paper: coming soon” with the actual Hugging Face paper page link for RouteWeaver, so the model card correctly references the technical report. No other changes are made.

Update paper link in model cardc1003cc6

e2rea1 changed pull request status to merged 1 day ago

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