---
pipeline_tag: text-generation
inference: true
widget:
- text: "What's lemur's favorite fruit?"
  example_title: Lemur favorite fruit
  group: Python
- text: 'Write a Python function to merge two sorted lists into one sorted list without using any built-in sort functions.'
  example_title: Merge Sort
  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>


Open large language models (LLMs) have traditionally been tailored for either textual or code-related tasks, with limited ability to effectively balance both. However, many complex language applications, particularly language model agents, demand systems with a multifaceted skill set encompassing understanding, reasoning, planning, coding, and context grounding. 

In this work, we introduce **Lemur-70B-v1** and **Lemur-70B-chat-v1**, the state-of-the-art open pretrained and supervised fine-tuned large language models balancing text and code intelligence.

<div align="center">
  <img src="https://huggingface.co/datasets/OpenLemur/assets/resolve/main/lemur_performance.png">
</div>


## 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.