metadata

license: other
license_name: nakshatra-license
license_link: LICENSE
pipeline_tag: text-generation
language:
  - en
tags:
  - Nakshatra
base_model:
  - OEvortex/HelpingAI2-6B

Nakshatra: Human-like Conversational AI Prototype

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.

Usage Code

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." },  
    { "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

# %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_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)