Instructions to use saishshinde15/Clyrai_Vortex_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saishshinde15/Clyrai_Vortex_GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("saishshinde15/Clyrai_Vortex_GGUF", dtype="auto")

llama-cpp-python

How to use saishshinde15/Clyrai_Vortex_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="saishshinde15/Clyrai_Vortex_GGUF",
	filename="unsloth.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use saishshinde15/Clyrai_Vortex_GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf saishshinde15/Clyrai_Vortex_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf saishshinde15/Clyrai_Vortex_GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf saishshinde15/Clyrai_Vortex_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf saishshinde15/Clyrai_Vortex_GGUF: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 saishshinde15/Clyrai_Vortex_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saishshinde15/Clyrai_Vortex_GGUF: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 saishshinde15/Clyrai_Vortex_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saishshinde15/Clyrai_Vortex_GGUF:Q4_K_M

Use Docker

docker model run hf.co/saishshinde15/Clyrai_Vortex_GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use saishshinde15/Clyrai_Vortex_GGUF with Ollama:
```
ollama run hf.co/saishshinde15/Clyrai_Vortex_GGUF:Q4_K_M
```

Unsloth Studio

How to use saishshinde15/Clyrai_Vortex_GGUF 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 saishshinde15/Clyrai_Vortex_GGUF 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 saishshinde15/Clyrai_Vortex_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for saishshinde15/Clyrai_Vortex_GGUF to start chatting

How to use saishshinde15/Clyrai_Vortex_GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf saishshinde15/Clyrai_Vortex_GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "saishshinde15/Clyrai_Vortex_GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use saishshinde15/Clyrai_Vortex_GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf saishshinde15/Clyrai_Vortex_GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default saishshinde15/Clyrai_Vortex_GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use saishshinde15/Clyrai_Vortex_GGUF with Docker Model Runner:
```
docker model run hf.co/saishshinde15/Clyrai_Vortex_GGUF:Q4_K_M
```

Lemonade

How to use saishshinde15/Clyrai_Vortex_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saishshinde15/Clyrai_Vortex_GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Clyrai_Vortex_GGUF-Q4_K_M

List all available models

lemonade list

Clyrai Vortex GGUF (4-bit )

Developed by: clyrai
License: apache-2.0
Fine-tuned from: saishshinde15/Clyrai_Vortex
Formats: GGUF ( 4-bit)

Overview

Clyrai Vortex GGUF is a highly optimized and efficient reasoning model, designed for advanced logical inference, structured problem-solving, and knowledge-driven decision-making. As part of the Vortex Family, this model excels in complex multi-step reasoning, detailed explanations, and high-context understanding across various domains.

Built upon fine-tuning on premium datasets, Clyrai Vortex GGUF demonstrates:

Superior logical consistency for tackling complex queries
Clear, step-by-step reasoning in problem-solving tasks
Accurate and well-grounded responses, ensuring factual reliability
Enhanced long-form understanding, making it ideal for in-depth research and analysis

With 4-bit optimizations, this model offers scalable performance, balancing high precision with efficiency, making it suitable for both cloud and edge deployment.

Key Features

Advanced fine-tuning on high-quality datasets for enhanced logical inference and structured reasoning.
Optimized for step-by-step explanations, improving response clarity and accuracy.
High efficiency across devices, with GGUF 16-bit for precision and GGUF 4-bit for lightweight deployment.
Fast and reliable inference, ensuring minimal latency while maintaining high performance.
Multi-turn conversation coherence, enabling deep contextual understanding in dialogue-based AI applications.
Scalable for various use cases, including AI tutoring, research, decision support, and autonomous agents.

Usage

For best results, use the following system instruction:

"You are an advanced AI assistant. Provide answers in a clear, step-by-step manner."

Downloads last month: 17

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for saishshinde15/Clyrai_Vortex_GGUF

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

saishshinde15/Clyrai_Base_Reasoning

Finetuned

saishshinde15/Clyrai_Vortex

Quantized

(2)

this model

Collection including saishshinde15/Clyrai_Vortex_GGUF

TBH.AI Vortex

Collection

SFT Models which is tuned on high quality dataset • 2 items • Updated Mar 3, 2025