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license: apache-2.0
language:
- aa
- ae
- am
- en
- es
- ar
- ja
- eo
- fr
- ru
pipeline_tag: text-generation
tags:
- nova
- ai
- nlop
- nexiloop
- llama
- llm
- novaai
- ainlop
- nlopai
- nexai
---
# Nexiloop Nova Model: Fully Open Source
**License:** Apache-2.0
**Datasets:**
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
- OpenAssistant/oasst_top1_2023-08-25
**Language:** English
---
<div align="center">
# Nexiloop Nova-1.1B
**Open Source and Ready for Use**
Fully optimized for various applications with a compact architecture.
</div>
[GitHub Repository](https://github.com/mohameodo/nova)
---
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.
### **Features:**
- **Optimized for Compact Systems:** With just 1.1B parameters, Nexiloop Nova is perfect for applications where memory and computation are limited.
- **Pretraining:** The model has been pre-trained on the **SlimPajama-627B** dataset, fine-tuned for even better conversational abilities.
### **Training Overview:**
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.
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**.
---
### **How to Use Nexiloop Nova Model**
To use Nexiloop Nova, you'll need **transformers>=4.34**. Below is a simple example showing how to integrate the model into your application.
#### Example Code:
```bash
# Install necessary libraries
pip install transformers==4.34
pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="nexiloop/nova", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# ...
``` |