Instructions to use OEvortex/Nakshatra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use OEvortex/Nakshatra with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OEvortex/Nakshatra:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OEvortex/Nakshatra:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OEvortex/Nakshatra:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OEvortex/Nakshatra:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf OEvortex/Nakshatra:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OEvortex/Nakshatra:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf OEvortex/Nakshatra:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OEvortex/Nakshatra:Q4_K_M

Use Docker

docker model run hf.co/OEvortex/Nakshatra:Q4_K_M

LM Studio
Jan

vLLM

How to use OEvortex/Nakshatra with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OEvortex/Nakshatra"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OEvortex/Nakshatra",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OEvortex/Nakshatra:Q4_K_M

Ollama
How to use OEvortex/Nakshatra with Ollama:
```
ollama run hf.co/OEvortex/Nakshatra:Q4_K_M
```

Unsloth Studio

How to use OEvortex/Nakshatra with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for OEvortex/Nakshatra to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for OEvortex/Nakshatra to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OEvortex/Nakshatra to start chatting

Docker Model Runner
How to use OEvortex/Nakshatra with Docker Model Runner:
```
docker model run hf.co/OEvortex/Nakshatra:Q4_K_M
```

Lemonade

How to use OEvortex/Nakshatra with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OEvortex/Nakshatra:Q4_K_M

Run and chat with the model

lemonade run user.Nakshatra-Q4_K_M

List all available models

lemonade list

Atomic Chat

Abhaykoul commited on Sep 21, 2024

Commit

793873f

verified ·

1 Parent(s): f24e9a3

Update README.md

Browse files

Files changed (1) hide show

README.md +7 -7

README.md CHANGED Viewed

@@ -13,12 +13,12 @@ base_model:
 # Nakshatra: Human-like Conversational AI Prototype
-![logo](https://huggingface.co/Abhaykoul/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.
 - 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.
@@ -34,9 +34,9 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 # Load the Nakshatra model
-model = AutoModelForCausalLM.from_pretrained("Abhaykoul/Nakshatra", trust_remote_code=True)
 # Load the tokenizer
-tokenizer = AutoTokenizer.from_pretrained("Abhaykoul/Nakshatra", trust_remote_code=True)
 # Define the chat input
 chat = [
@@ -75,9 +75,9 @@ from webscout.Local import formats
 from webscout.Local.samplers import SamplerSettings
 # Download the model
-repo_id = "Abhaykoul/Nakshatra"
-filename = "nakshatra-q5_0.gguf"
-model_path = download_model(repo_id, filename, token="")
 # Load the model
 model = Model(model_path, n_gpu_layers=40)

 # 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.
 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 = [
 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)