86 lines
2.5 KiB
Markdown
86 lines
2.5 KiB
Markdown
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---
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pipeline_tag: text-generation
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inference: true
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widget:
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- text: "What's lemur's favorite fruit?"
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example_title: Lemur favorite fruit
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group: Python
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- text: 'Write a Python function to merge two sorted lists into one sorted list without using any built-in sort functions.'
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example_title: Merge Sort
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group: Python
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license: cc-by-nc-4.0
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library_name: transformers
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tags:
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- text-generation
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- code
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- text-generation-inference
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language:
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- en
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---
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# lemur-70b-chat-v1
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<p align="center">
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<img src="https://huggingface.co/datasets/OpenLemur/assets/resolve/main/lemur_icon.png" width="300" height="300" alt="Lemur">
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</p>
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<div align="center">
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<img src="https://huggingface.co/datasets/OpenLemur/assets/resolve/main/lemur_chat_radar.png">
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</div>
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📄Paper: https://arxiv.org/abs/2310.06830
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👩💻Code: https://github.com/OpenLemur/Lemur
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## Use
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### Setup
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First, we have to install all the libraries listed in `requirements.txt` in [GitHub](https://github.com/OpenLemur/lemur-v1):
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```bash
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pip install -r requirements.txt
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```
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### Generation
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("OpenLemur/lemur-70b-chat-v1")
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model = AutoModelForCausalLM.from_pretrained("OpenLemur/lemur-70b-chat-v1", device_map="auto", load_in_8bit=True)
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# Text Generation Example
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prompt = """<|im_start|>system
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You are a helpful, respectful, and honest assistant.
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<|im_end|>
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<|im_start|>user
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What's a lemur's favorite fruit?<|im_end|>
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<|im_start|>assistant
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"""
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input = tokenizer(prompt, return_tensors="pt")
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output = model.generate(**input, max_length=50, num_return_sequences=1)
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
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print(generated_text)
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# Code Generation Example
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prompt = """<|im_start|>system
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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<|im_end|>
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<|im_start|>user
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Write a Python function to merge two sorted lists into one sorted list without using any built-in sort functions.<|im_end|>
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<|im_start|>assistant
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"""
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input = tokenizer(prompt, return_tensors="pt")
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output = model.generate(**input, max_length=200, num_return_sequences=1)
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generated_code = tokenizer.decode(output[0], skip_special_tokens=True)
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print(generated_code)
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```
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# License
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The model is licensed under a CC BY-NC-4.0 license focused on research use cases.
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# Acknowledgements
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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.
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