Instructions to use KATHIR2006/Zenthi-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KATHIR2006/Zenthi-AI with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="KATHIR2006/Zenthi-AI")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KATHIR2006/Zenthi-AI")
model = AutoModelForCausalLM.from_pretrained("KATHIR2006/Zenthi-AI")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KATHIR2006/Zenthi-AI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KATHIR2006/Zenthi-AI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KATHIR2006/Zenthi-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KATHIR2006/Zenthi-AI

SGLang

How to use KATHIR2006/Zenthi-AI with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "KATHIR2006/Zenthi-AI" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KATHIR2006/Zenthi-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "KATHIR2006/Zenthi-AI" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KATHIR2006/Zenthi-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KATHIR2006/Zenthi-AI with Docker Model Runner:
```
docker model run hf.co/KATHIR2006/Zenthi-AI
```

Zenthi-AI / README.md

KATHIR2006

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metadata

license:
  - apache-2.0
  - mit
base_model:
  - Qwen/Qwen2.5-0.5B-Instruct
  - Qwen/Qwen2.5-Coder-3B-Instruct
  - HuggingFaceTB/SmolVLM-Instruct
language:
  - en
datasets:
  - tatsu-lab/alpaca
  - databricks/databricks-dolly-15k
  - teknium/OpenHermes-2.5
  - stingning/ultrachat
metrics:
  - accuracy
library_name: transformers
tags:
  - text-generation
  - custom-slm
  - conversational
  - agentic-router
  - qlora
  - chroma-rag
  - local-ai
  - pytorch
  - vision
  - multimodal
  - code-generation
  - vlm
  - vqa
  - ocr

Zenthi-AI OS: Agentic Multi-Model Small Language Model (SLM)

Zenthi-AI is a production-grade, custom fine-tuned Small Language Model (SLM) conversational assistant. It is optimized for high-speed, local-first execution and acts as the intent-routing brain and synthesis engine of the Zenthi-AI Multi-Model Operating System.

This repository hosts the merged, full-precision model weights.

🚀 Key Features

Base Foundation: Built on the highly capable Qwen/Qwen2.5-0.5B-Instruct.
Parameter-Efficient Finetuning: Optimized via QLoRA (4-bit quantization) on a merged, cleaned dataset of Alpaca, Dolly 15K, OpenHermes, UltraChat, and ShareGPT.
Agentic Orchestrator Routing: Tuned specifically to act as a Router and Planner Agent, classifying query intents with high accuracy (CODE, VISION, RAG, SEARCH, KNOWLEDGE, COMPLEX).
Quantization-Ready: Quantized to GGUF format for local deployment (quantized size under 500 MB).
Local RAG Integration: Built to work in tandem with local ChromaDB embedding vector stores.
Web Search Coordination: Designed to synthesize real-time context fetched from local SearXNG search clients.
Memory Management: Keeps a windowed session history for conversational continuity.

📊 Evaluation & Routing Performance

The model's semantic routing accuracy was benchmarked across 500 unique evaluation test queries (100 queries per intent category) running on a local GPU:

Overall Routing Accuracy: 72.60%
Average Latency: 651.54 ms per query

Intent Category	Accuracy (%)	Target Expert Model
CODE	100.00%	`qwen2.5-coder:3b`
VISION	100.00%	`riven/smolvlm:latest`
SEARCH	99.00%	`qwen2.5:1.5b-instruct`
RAG	43.00%	`qwen2.5:1.5b-instruct`
KNOWLEDGE	21.00%	`qwen2.5:1.5b-instruct`

💻 Local Usage & Integration

1. Ollama Deployment (GGUF)

To run Zenthi-AI locally in Ollama:

Create a Modelfile with the system prompt:

FROM zenthi-ai:latest
PARAMETER temperature 0.7
PARAMETER top_p 0.9
SYSTEM """I am Zenthi-AI OS, a production-grade Agentic Multi-Model AI Operating System. I deliver accurate, secure, maintainable, and production-ready solutions by coordinating specialized AI capabilities."""

Build and run:

ollama create Zenthi-AI -f Modelfile
ollama run Zenthi-AI

2. Python Transformers API

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "KATHIR2006/zenthi-ai"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# Start conversation
messages = [
    {"role": "system", "content": "You are Zenthi-AI OS, a production-grade Agentic Multi-Model AI Operating System."},
    {"role": "user", "content": "Explain photosynthesis simply."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=512)
response = tokenizer.batch_decode(generated_ids[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)[0]
print(response)

🛠️ Fine-Tuned Expert Adapters

This repository also hosts the fine-tuned LoRA adapters for the specialized expert models of the Zenthi-AI OS:

1. Code Expert Adapters (`code-adapters/`)

Base Model: Qwen/Qwen2.5-Coder-3B-Instruct
Dataset: Custom programming and instruction dataset (1,200 training steps)
Final Loss: 0.1843
Usage: Optimized for React, Node.js, Python, MERN stack development, reviews, and refactoring.

2. Vision Expert Adapters (`vision-adapters/`)

Base Model: HuggingFaceTB/SmolVLM-Instruct
Dataset: Synthetic VQA shape and color recognition dataset (100 training steps)
Final Loss: 0.9077
Usage: Fine-tuned for OCR, visual question-answering, and image analysis.

⚖️ Licenses & Compliance

This project is dual-licensed:

LLM Model Weights & Adaptations: Licensed under the Apache License 2.0 (in compliance with the base Qwen2.5 license).
RAG Engine, Multi-Agent Framework, & Backend Codebase: Licensed under the MIT License.