README.md

---
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
- text-generation-inference
language:
- en
---

# lemur-70b-chat-v1

<p align="center">
  <img src="https://huggingface.co/datasets/OpenLemur/assets/resolve/main/lemur_icon.png" width="300" height="300" alt="Lemur">
</p>


## Model Summary

- **Repository:** [OpenLemur/lemur-v1](https://github.com/OpenLemur/lemur-v1)
- **Project Website:** [xlang.ai](https://www.xlang.ai/)
- **Paper:** [Coming soon](https://www.xlang.ai/)
- **Point of Contact:** [mail@xlang.ai](mailto:mail@xlang.ai)


## Use

### Setup

First, we have to install all the libraries listed in `requirements.txt` in [GitHub](https://github.com/OpenLemur/lemur-v1):

```bash
pip install -r requirements.txt
```

### Generation

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenLemur/lemur-70b-chat-v1")
model = AutoModelForCausalLM.from_pretrained("OpenLemur/lemur-70b-chat-v1", device_map="auto", load_in_8bit=True)

# Text Generation Example
prompt = "What's lemur's favorite fruit?"
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 = "Write a Python function to merge two sorted lists into one sorted list without using any built-in sort functions."
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.