lemur-70b-v1
πPaper: https://arxiv.org/abs/2310.06830
π©βπ»Code: https://github.com/OpenLemur/Lemur
Use
Setup
First, we have to install all the libraries listed in requirements.txt
in GitHub:
pip install -r requirements.txt
Intended use
Since it is not trained on instruction following corpus, it won't respond well to questions like "What is the Python code to do quick sort?".
Generation
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("OpenLemur/lemur-70b-v1")
model = AutoModelForCausalLM.from_pretrained("OpenLemur/lemur-70b-v1", device_map="auto", load_in_8bit=True)
# Text Generation Example
prompt = "The world is "
input = tokenizer(prompt, return_tensors="pt")
output = model.generate(**input, max_length=50, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
# Code Generation Example
prompt = """
def factorial(n):
if n == 0:
return 1
"""
input = tokenizer(prompt, return_tensors="pt")
output = model.generate(**input, max_length=200, num_return_sequences=1)
generated_code = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_code)
License
The model is licensed under the Llama-2 community license agreement.
Acknowledgements
The Lemur project is an open collaborative research effort between XLang Lab and Salesforce Research. We thank Salesforce, Google Research and Amazon AWS for their gift support.
- Downloads last month
- 1,492
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.