hongyin/chatbloom-7b

chatbloom-7b

This is a RLHF enhanced bloom model (chatbloom), fine-tuned based on bloom-7b (Muennighoff et al.). This model only uses English QA datasets for RLHF training, which improves the understanding and generation of English.

Usage

If you don't have a good GPU (mem > 20G) then use the code below:

# pip install -q transformers accelerate
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "hongyin/chatbloom-7b"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)

inputs = tokenizer.encode("Paraphrasing the text: I love you.", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

Original ouput: Paraphrasing the text: I love you. I love you. I love you. I love
ChatBloom ouput: Paraphrasing the text: I love you. I am a good person.

If you have a good GPU (mem > 20G) then use the code below:

# pip install -q transformers accelerate
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "hongyin/chatbloom-7b"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto")

inputs = tokenizer.encode("Paraphrasing the text: I love you.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

Original ouput: Paraphrasing the text: I love you. I love you. I love you. I love
ChatBloom ouput: Paraphrasing the text: I love you. I am a good person.

Bibtex entry and citation info

Please cite if you find it helpful.

@article{zhu2023metaaid,
  title={MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models},
  author={Zhu, Hongyin},
  journal={arXiv preprint arXiv:2302.13173},
  year={2023}
}

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license: other