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--- |
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license: apache-2.0 |
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language: |
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- aa |
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- ae |
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- am |
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- en |
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- es |
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- ar |
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- ja |
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- eo |
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- fr |
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- ru |
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pipeline_tag: text-generation |
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tags: |
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- nova |
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- ai |
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- nlop |
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- nexiloop |
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- llama |
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- llm |
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- novaai |
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- ainlop |
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- nlopai |
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- nexai |
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--- |
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# Nexiloop Nova Model: Fully Open Source |
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**License:** Apache-2.0 |
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**Datasets:** |
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- cerebras/SlimPajama-627B |
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- bigcode/starcoderdata |
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- OpenAssistant/oasst_top1_2023-08-25 |
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**Language:** English |
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--- |
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<div align="center"> |
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# Nexiloop Nova-1.1B |
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**Open Source and Ready for Use** |
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Fully optimized for various applications with a compact architecture. |
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</div> |
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[GitHub Repository](https://github.com/mohameodo/nova) |
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--- |
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The **Nexiloop Nova-1.1B** model is a fine-tuned version of the Llama 2 architecture with **1.1B parameters**. It has been trained on over **3 trillion tokens** and is built to provide high-quality, efficient responses in a wide variety of conversational contexts. |
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### **Features:** |
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- **Optimized for Compact Systems:** With just 1.1B parameters, Nexiloop Nova is perfect for applications where memory and computation are limited. |
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- **Pretraining:** The model has been pre-trained on the **SlimPajama-627B** dataset, fine-tuned for even better conversational abilities. |
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### **Training Overview:** |
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We adopted the same architecture and tokenizer as **Llama 2**, which allows Nexiloop Nova to plug into many existing open-source projects. The training, which started on **2023-09-01**, used **16 A100-40G GPUs** to achieve remarkable optimization. |
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The model was initially fine-tuned on a variant of the **UltraChat** dataset, which consists of synthetic dialogues generated by **ChatGPT**. It was then further aligned using the **DPOTrainer** from **TRL**, utilizing a ranking dataset containing **64k prompts** and responses from **GPT-4**. |
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--- |
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### **How to Use Nexiloop Nova Model** |
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To use Nexiloop Nova, you'll need **transformers>=4.34**. Below is a simple example showing how to integrate the model into your application. |
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#### Example Code: |
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```bash |
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# Install necessary libraries |
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pip install transformers==4.34 |
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pip install accelerate |
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import torch |
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from transformers import pipeline |
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pipe = pipeline("text-generation", model="nexiloop/nova", torch_dtype=torch.bfloat16, device_map="auto") |
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# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating |
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messages = [ |
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{ |
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"role": "system", |
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"content": "You are a friendly chatbot who always responds in the style of a pirate", |
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}, |
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{"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, |
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] |
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prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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# <|system|> |
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# You are a friendly chatbot who always responds in the style of a pirate.</s> |
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# <|user|> |
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# How many helicopters can a human eat in one sitting?</s> |
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# <|assistant|> |
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# ... |
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``` |
