Instructions to use songff/Pilot-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use songff/Pilot-3B with Transformers:

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

pipe = pipeline("text-generation", model="songff/Pilot-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("songff/Pilot-3B")
model = AutoModelForCausalLM.from_pretrained("songff/Pilot-3B")
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

vLLM

How to use songff/Pilot-3B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/songff/Pilot-3B

SGLang

How to use songff/Pilot-3B 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 "songff/Pilot-3B" \
    --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": "songff/Pilot-3B",
		"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 "songff/Pilot-3B" \
        --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": "songff/Pilot-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use songff/Pilot-3B with Docker Model Runner:
```
docker model run hf.co/songff/Pilot-3B
```

Add library name

by nielsr HF Staff - opened Jun 11, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-4

Files changed (1) hide show

README.md +5 -4

README.md CHANGED Viewed

@@ -1,12 +1,13 @@
 ---
-license: apache-2.0
-datasets:
-- songff/GenerAlign
 base_model:
 - meta-llama/Llama-3.2-3B-Instruct
 language:
 - en
 pipeline_tag: text-generation
 ---
 Pilot-3B is designed to be a draft model in efficient preference alignment of LLMs for its small size while high performance in general domains. It is trained from [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on [GenerAlign](https://huggingface.co/datasets/songff/GenerAlign).
@@ -28,7 +29,7 @@ If you find this work useful, please consider citing:
 ```bibtex
 @misc{song2025well,
   title={Well Begun is Half Done: Low-resource Preference Alignment by Weak-to-Strong Decoding},
-  author={Song, Feifan and Wei, Shaohang and Luo, Wen and Fan, Yuxuan and Liu, Tianyu and Wang, Guoyin and Wang, Houfeng},
   year={2025},
   eprint={2506.07434},
   archivePrefix={arXiv},

 ---
 base_model:
 - meta-llama/Llama-3.2-3B-Instruct
+datasets:
+- songff/GenerAlign
 language:
 - en
+license: apache-2.0
 pipeline_tag: text-generation
+library_name: transformers
 ---
 Pilot-3B is designed to be a draft model in efficient preference alignment of LLMs for its small size while high performance in general domains. It is trained from [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on [GenerAlign](https://huggingface.co/datasets/songff/GenerAlign).
 ```bibtex
 @misc{song2025well,
   title={Well Begun is Half Done: Low-resource Preference Alignment by Weak-to-Strong Decoding},
+  author={Song, Feifan and Wei, Shaohang and Luo, Wen and Fan, Yuxuan, Liu, Tianyu and Wang, Guoyin and Wang, Houfeng},
   year={2025},
   eprint={2506.07434},
   archivePrefix={arXiv},