Instructions to use bunsenfeng/PFA_switcher_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bunsenfeng/PFA_switcher_1 with Transformers:

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

pipe = pipeline("text-generation", model="bunsenfeng/PFA_switcher_1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bunsenfeng/PFA_switcher_1", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bunsenfeng/PFA_switcher_1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bunsenfeng/PFA_switcher_1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bunsenfeng/PFA_switcher_1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bunsenfeng/PFA_switcher_1

SGLang

How to use bunsenfeng/PFA_switcher_1 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 "bunsenfeng/PFA_switcher_1" \
    --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": "bunsenfeng/PFA_switcher_1",
		"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 "bunsenfeng/PFA_switcher_1" \
        --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": "bunsenfeng/PFA_switcher_1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bunsenfeng/PFA_switcher_1 with Docker Model Runner:
```
docker model run hf.co/bunsenfeng/PFA_switcher_1
```

Improve model card for Switch Generation model with paper, GitHub links, usage, and metadata

by nielsr HF Staff - opened Oct 17, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+64

-69

nielsr

Oct 17, 2025

This PR significantly improves the model card for the "Switch Generation" model.

Key updates include:

Comprehensive Description: The boilerplate text has been replaced with a detailed summary derived from the paper's abstract, explaining the novel "Switch Generation" concept.
Metadata Enrichment:
- The pipeline_tag: text-generation has been added for better discoverability on the Hugging Face Hub.
- Relevant tags such as llama, model-collaboration, and instruction-following have been included.
- The base_model has been explicitly listed (allenai/Llama-3.1-Tulu-3-8B).
- The license is set to other as no explicit license was found in the source materials.
Linked Resources: Direct links to the academic paper (Don't Throw Away Your Pretrained Model) and the associated GitHub repository (https://github.com/BunsenFeng/switch_generation) have been added.
Getting Started Guide: A "How to Get Started" section, including code snippets for environment setup and inference, has been extracted directly from the GitHub README.

These changes make the model card much more informative and user-friendly for researchers and practitioners.

Improve model card for Switch Generation model with paper, GitHub links, usage, and metadata2f5dbdb7

bunsenfeng changed pull request status to merged Oct 17, 2025

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