Create README.md

Update README.md

Create README.md

README.md

@@ -0,0 +1,77 @@
+---
+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>
+
+
+## Table of Contents
+
+1. [Model Summary](##model-summary)
+2. [Use](##use)
+3. [License](##license)
+
+
+## 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.