---
license: other
license_name: nakshatra-license
license_link: LICENSE
pipeline_tag: text-generation
language:
- en
tags:
- Nakshatra
---

# Nakshatra: Human-like Conversational AI Prototype

![logo](https://huggingface.co/OEvortex/Nakshatra/resolve/main/Designer.png)

## Overview  
Nakshatra is a groundbreaking prototype AI model, boasting **10x** better human-like responses compared to the previous HelpingAI models. Designed by **Abhay Koul (OEvortex)**, Nakshatra leverages advanced conversational techniques to deliver highly coherent, empathetic, and contextually aware interactions, making it a major leap forward in AI-human interaction.

- Delivers near-human conversational quality and responsiveness.- Delivers near-human conversational quality and responsiveness.
- Exhibits deep contextual understanding and emotional intelligence in interactions.
- Aimed at providing more natural, emotionally intuitive dialogue experiences.- Aimed at providing more natural, emotionally intuitive dialogue experiences.

## Methodology  
Nakshatra employs a combination of the following techniques to achieve its remarkable conversational capabilities:
- **Supervised Learning**: Trained with vast dialogue datasets, including those with emotional annotations, to ensure it can handle a wide range of conversational contexts.
- **Human-like Conversation Training**: Fine-tuned to imitate natural human conversational patterns.
- **Prototype Optimization**: This version is still in the prototype phase but showcases significant advancements in language coherence, tone, and emotional sensitivity.


## Limitations

While Nakshatra represents a significant advancement in conversational AI, it is important to acknowledge its limitations:

- **Prototype Status**: Nakshatra is currently in the prototype phase, which means it may not be fully optimized for all conversational scenarios. Users should be aware that further refinements and updates are expected.

- **Factual Accuracy**: The model is designed to mimic human conversational styles and may generate responses that sound plausible but are factually incorrect. Users should verify critical information from reliable sources.

- **Contextual Limitations**: Although Nakshatra exhibits deep contextual understanding, it may still struggle with complex or nuanced topics, leading to misunderstandings or irrelevant responses.

- **Bias and Ethical Considerations**: Like all AI models, Nakshatra may inadvertently reflect biases present in the training data. Users should be mindful of this and approach interactions with a critical perspective.

- **Dependence on Input Quality**: The quality of the model's responses is highly dependent on the clarity and context of the input it receives. Ambiguous or poorly structured queries may result in less coherent outputs.

## Usage Code  
```python  
import torch  
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Nakshatra model  
model = AutoModelForCausalLM.from_pretrained("OEvortex/Nakshatra", trust_remote_code=True)  
# Load the tokenizer  
tokenizer = AutoTokenizer.from_pretrained("OEvortex/Nakshatra", trust_remote_code=True)  

# Define the chat input  
chat = [  
    { "role": "system", "content": "You are Nakshatra, a human-like conversational AI. Answer in the most human way possible, Provide concise and to-the-point answers." },  
    { "role": "user", "content": "Introduce yourself!" }  
]

inputs = tokenizer.apply_chat_template(  
    chat,  
    add_generation_prompt=True,  
    return_tensors="pt"  
).to(model.device)

# Generate text  
outputs = model.generate(  
    inputs,  
    max_new_tokens=256,  
    do_sample=True,  
    temperature=0.6,  
    top_p=0.9,  
    eos_token_id=tokenizer.eos_token_id  
)

response = outputs[0][inputs.shape[-1]:]  
print(tokenizer.decode(response, skip_special_tokens=True))
```

## Using the Model with GGUF  
```python  
# %pip install -U 'webscout[local]' -q  

from webscout.Local.utils import download_model  
from webscout.Local.model import Model  
from webscout.Local.thread import Thread  
from webscout.Local import formats  
from webscout.Local.samplers import SamplerSettings  

# Download the model  
repo_id = "OEvortex/Nakshatra"  
filename = "nakshatra-q4_k_m.gguf"  
model_path = download_model(repo_id, filename, token=None)  

# Load the model  
model = Model(model_path, n_gpu_layers=40)  

# Define the system prompt  
system_prompt = "You are Nakshatra, a human-like conversational AI. Answer in the most human way possible."

# Create a chat format with your system prompt  
nakshatra_format = formats.llama3.copy()  
nakshatra_format['system_prompt'] = system_prompt  nakshatra_format['system_content'] = system_prompt  

# Define your sampler settings (optional)  
sampler = SamplerSettings(temp=0.7, top_p=0.9)  

# Create a Thread with the custom format and sampler  
thread = Thread(model, nakshatra_format, sampler=sampler)  

# Start interacting with the model  
thread.interact(header="🌟 Nakshatra - Human-like AI Prototype 🚀", color=True)  
```