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BBuf/RWKV-4-World-1B5
RWKV-4-World-1B5 is a machine learning model from BBuf. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
Downloads · 30 days
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From the Hugging Face model README
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("BBuf/RWKV-4-World-1B5")
tokenizer = AutoTokenizer.from_pretrained("BBuf/RWKV-4-World-1B5", trust_remote_code=True)
text = "\nIn a shocking finding, scientist discovered a herd of dragons living in a remote, previously unexplored valley, in Tibet. Even more surprising to the researchers was the fact that the dragons spoke perfect Chinese."
prompt = f'Question: {text.strip()}\n\nAnswer:'
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(inputs["input_ids"], max_new_tokens=256)
print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
output:
Question: In a shocking finding, scientist discovered a herd of dragons living in a remote, previously unexplored valley, in Tibet. Even more surprising to the researchers was the fact that the dragons spoke perfect Chinese.
Answer: The researchers were surprised to discover a herd of dragons living in a remote, previously unexplored valley in Tibet
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("BBuf/RWKV-4-World-1B5", torch_dtype=torch.float16).to(0)
tokenizer = AutoTokenizer.from_pretrained("BBuf/RWKV-4-World-1B5", trust_remote_code=True)
text = "你叫什么名字?"
prompt = f'Question: {text.strip()}\n\nAnswer:'
inputs = tokenizer(prompt, return_tensors="pt").to(0)
output = model.generate(inputs["input_ids"], max_new_tokens=40)
print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
output:
Question: 你叫什么名字?
Answer: 我是一个人工智能语言模型,没有名字。