metadata
pipeline_tag: text-generation
inference: true
widget:
- text: 'def factorial(n):'
example_title: Factorial
group: Python
- text: 'def recur_fibo(n):'
example_title: Recursive Fibonacci
group: Python
license: llama2
library_name: transformers
tags:
- text-generation
- code
language:
- en
lemur-70b-v1
📄Paper: https://arxiv.org/abs/2310.06830
👩💻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.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 54.03 |
| ARC (25-shot) | 64.33 |
| HellaSwag (10-shot) | 85.72 |
| MMLU (5-shot) | 65.85 |
| TruthfulQA (0-shot) | 44.78 |
| Winogrande (5-shot) | 83.03 |
| GSM8K (5-shot) | 28.73 |
| DROP (3-shot) | 5.74 |