Fhrozen/Seed-X-PPO-7B-ONNX

Seed-X-PPO-7B

Introduction

We are excited to introduce Seed-X, a powerful series of open-source multilingual translation language models, including an instruction model, a reinforcement learning model, and a reward model. It pushes the boundaries of translation capabilities within 7 billion parameters.

https://huggingface.co/ByteDance-Seed/Seed-X-PPO-7B with ONNX weights.

Usage

Python with Optimum and ORT:

import os

import onnxruntime as ort
from optimum.onnxruntime import ORTModelForCausalLM
from transformers import AutoTokenizer, PretrainedConfig, GenerationConfig

def main():
    work_dir = "[huggingface/dir]"
    config = PretrainedConfig.from_pretrained(work_dir)
    gen_config = GenerationConfig.from_pretrained(work_dir)
    suffix = "_q4"

    model_path = os.path.join(work_dir, "onnx", f"model{suffix}.onnx")
    use_gpu = True
    providers = [
        ("CUDAExecutionProvider", {"device_id": 0})
    ] if use_gpu else []
    providers.append("CPUExecutionProvider")
    
    sess_options = ort.SessionOptions()
    ort_model = ort.InferenceSession(model_path, sess_options, providers=providers)
    llm_model = ORTModelForCausalLM(
        session=ort_model,
        config=config,
        generation_config=gen_config,
        use_io_binding=True,
        use_cache=True,
    )

    tokenizer = AutoTokenizer.from_pretrained(work_dir)
    prompt = "Translate the following English sentence into Spanish:\nYou are using a model of type mistral to instantiate a model of type . <es>"
    inputs = tokenizer([prompt], return_tensors="pt").to("cuda")

    print("Input prompt: ", prompt)

    generated_ids = llm_model.generate(
        **inputs,
        max_new_tokens=512,
        num_beams=4,
        do_sample=False,
        temperature=1.0,
    )

    predicts = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    print("Output: ", "".join(predicts[len(prompt):]))
    return

if __name__ == "__main__":
    main()