Instructions to use songff/SinglePO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use songff/SinglePO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="songff/SinglePO") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("songff/SinglePO") model = AutoModelForCausalLM.from_pretrained("songff/SinglePO", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use songff/SinglePO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "songff/SinglePO" # 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/SinglePO", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/songff/SinglePO
- SGLang
How to use songff/SinglePO 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/SinglePO" \ --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/SinglePO", "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/SinglePO" \ --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/SinglePO", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use songff/SinglePO with Docker Model Runner:
docker model run hf.co/songff/SinglePO
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aa73958 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | from transformers import PreTrainedTokenizerFast
class SinglePOTokenizer(PreTrainedTokenizerFast):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def get_context(
self,
raw_instruction,
rule_description,
):
prompt = "You are an expert prompt engineer." + " "
prompt += "Please help me optimize this prompt to get better response:\n\n[The Start of Raw Prompt]\n{}\n[The End of Raw Prompt]".format(raw_instruction)
prompt += "\n\nYou should optimize this prompt by {}".format(rule_description)
context = self.apply_chat_template(
[
{
"role": "user",
"content": prompt,
}
],
add_generation_prompt=True,
tokenize=False,
) + "The Optimized Prompt:\n\n[The Start of Optimized Prompt"
return context
def parse_output(
self,
output_text,
raw_instruction = "", # recommend to provide, so when some error happened, we can still use the raw instruction
):
better_instruction = "The Optimized Prompt:\n\n[The Start of Optimized Prompt" + output_text
if "[The Start of Optimized Prompt]" in better_instruction:
better_instruction = better_instruction[better_instruction.index("[The Start of Optimized Prompt]") + len("[The Start of Optimized Prompt]"):]
if better_instruction.startswith("\n"):
better_instruction = better_instruction[1:]
if "[The End of Optimized Prompt]" in better_instruction:
better_instruction = better_instruction[:better_instruction.index("[The End of Optimized Prompt]")]
if better_instruction.endswith("\n"):
better_instruction = better_instruction[:-1]
if "The Optimized Prompt:" in better_instruction: # almost error happened
better_instruction = better_instruction[:better_instruction.index("The Optimized Prompt:")]
if better_instruction.strip() == "": # some error may happen in optimization, so use the raw instruction
better_instruction = raw_instruction
if "The Optimized" in better_instruction: # still some error happened
better_instruction = raw_instruction
return better_instruction |