My model is a state-of-the-art language processing AI designed to understand and generate human-like text. It leverages deep learning algorithms to engage in a wide range of language tasks, providing users with information, recommendations, and even casual conversation. With a broad knowledge base and nuanced understanding of context, my capabilities enable me to assist with various inquiries and perform complex language-based tasks effectively.

How to use？

from transformers import AutoModelForCausalLM, AutoTokenizer

from transformers.generation import GenerationConfig

import torch

model = AutoModelForCausalLM.from_pretrained( 'TwT-6/cr-model', attn_implementation="flash_attention_2", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto").eval()

tokenizer = AutoTokenizer.from_pretrained('TwT-6/cr-model', trust_remote_code=True)

inputs = '你好'

inputs = f'<|omni_start|>### User:\n{inputs}\n\n### Assistant:\n'

inputs = tokenizer(inputs, return_tensors="pt").to('cuda')

output_ids = model.generate(**inputs)[0].cpu()

output = tokenizer.decode(output_ids[inputs.input_ids.shape[-1]:])

print(output)

Detailed results can be found here