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---
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
```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." },
{ "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_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)
``` |