--- 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: cc-by-nc-4.0 library_name: transformers tags: - text-generation - code - text-generation-inference language: - en --- # lemur-70b-chat-v1

Lemur

📄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](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 = """<|im_start|>system You are a helpful, respectful, and honest assistant. <|im_end|> <|im_start|>user What's a lemur's favorite fruit?<|im_end|> <|im_start|>assistant """ 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 = """<|im_start|>system Below is an instruction that describes a task. Write a response that appropriately completes the request. <|im_end|> <|im_start|>user Write a Python function to merge two sorted lists into one sorted list without using any built-in sort functions.<|im_end|> <|im_start|>assistant """ 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 a CC BY-NC-4.0 license focused on research use cases. # Acknowledgements The Lemur project is an open collaborative research effort between [XLang Lab](https://www.xlang.ai/) and Salesforce Research. We thank Salesforce, Google Research and Amazon AWS for their gift support. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenLemur__lemur-70b-chat-v1) | Metric | Value | |-----------------------|---------------------------| | Avg. | 57.1 | | ARC (25-shot) | 66.98 | | HellaSwag (10-shot) | 85.73 | | MMLU (5-shot) | 65.99 | | TruthfulQA (0-shot) | 56.58 | | Winogrande (5-shot) | 81.69 | | GSM8K (5-shot) | 35.33 | | DROP (3-shot) | 7.4 |